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/**
 * @file cudnn_graph.h
 * @brief cuDNN Graph library: core definitions, enums, backend descriptor operations, and handle management.
 * @since cuDNN 9.0.0
 */

/*
 *  cudnn_graph : cuDNN's basic definitions operations.
 */

#if !defined(CUDNN_GRAPH_H_)
#define CUDNN_GRAPH_H_

/* See: C5105_WINDOWS_SDK_WORKAROUND in src/common/include/cudnn_dls.h */
#ifdef _WIN32
#pragma warning(push)
#pragma warning(disable: 5105)
#endif
#include <cuda_runtime_api.h>
#include <library_types.h>
#ifdef _WIN32
#pragma warning(pop)
#endif

#include <stdint.h>

#include "cudnn_version.h"

/* These version numbers are autogenerated, do not edit manually. */
#define CUDNN_GRAPH_MAJOR 9
#define CUDNN_GRAPH_MINOR 25
#define CUDNN_GRAPH_PATCH 1

#if (CUDNN_GRAPH_MAJOR != CUDNN_MAJOR) || (CUDNN_GRAPH_MINOR != CUDNN_MINOR) || (CUDNN_GRAPH_PATCH != CUDNN_PATCHLEVEL)
#error Version mismatch in cuDNN GRAPH!!!
#endif

#ifndef CUDNNWINAPI
#ifdef _WIN32
#define CUDNNWINAPI __stdcall
#else
#define CUDNNWINAPI
#endif
#endif

/* Warnings for deprecated API-s are enabled using the CUDNN_WARN_DEPRECATED macro */
#if defined(CUDNN_WARN_DEPRECATED) && (defined(__GNUC__) || defined(__clang__))
/* GCC, Intel C/C++, Cray C/C++, CLANG, IBM XL C/C++ little endian */
#define CUDNN_DEPRECATED __attribute__((deprecated))
#define CUDNN_DEPRECATED_ENUM __attribute__((deprecated))
#elif defined(CUDNN_WARN_DEPRECATED) && defined(_MSC_VER)
/* Microsoft Visual C++ */
#define CUDNN_DEPRECATED __declspec(deprecated)
#define CUDNN_DEPRECATED_ENUM __declspec(deprecated)
#elif defined(CUDNN_WARN_DEPRECATED) && (__cplusplus >= 201402L)
/* C++14 compilers */
#define CUDNN_DEPRECATED [[deprecated]]
#define CUDNN_DEPRECATED_ENUM [[deprecated]]
#else
/* No support for the deprecated attribute */
#define CUDNN_DEPRECATED
#define CUDNN_DEPRECATED_ENUM
#endif

#if defined(__cplusplus)
extern "C" {
#endif

struct cudnnContext;

/** @brief Opaque pointer to cuDNN library context.
 *  @since cuDNN 9.0.0
 */
typedef struct cudnnContext *cudnnHandle_t;

/** @brief Returns cuDNN library version (MAJOR*10000 + MINOR*100 + PATCH).
 *  @return The cuDNN version as an encoded integer.
 *  @since cuDNN 9.0.0
 */
size_t CUDNNWINAPI
cudnnGetVersion(void);

/** @brief Returns max supported GPU compute capability.
 *  @return The maximum supported device version.
 *  @since cuDNN 9.0.0
 */
size_t CUDNNWINAPI
cudnnGetMaxDeviceVersion(void);

/** @brief Returns CUDA Runtime version linked against cuDNN.
 *  @return The CUDA Runtime version.
 *  @since cuDNN 9.0.0
 */
/* Returns CUDA Runtime version statically linked against cudnn */
size_t CUDNNWINAPI
cudnnGetCudartVersion(void);

/** @brief Return status codes for cuDNN API calls.
 *
 *  Status codes are grouped by category: 0=success, 1xxx=initialization/version,
 *  2xxx=bad parameter, 3xxx=not supported, 4xxx=internal error, 5xxx=execution failed.
 *  @since cuDNN 9.0.0
 */
/*
 * CUDNN return codes
 */
typedef enum {
    CUDNN_STATUS_SUCCESS = 0, /**< Operation completed successfully. @since cuDNN 9.0.0 */

    /* Uncategorized errors */
    CUDNN_STATUS_NOT_INITIALIZED                = 1001, /**< cuDNN library not initialized. @since cuDNN 9.0.0 */
    CUDNN_STATUS_SUBLIBRARY_VERSION_MISMATCH    = 1002, /**< Sub-library version mismatch. @since cuDNN 9.0.0 */
    CUDNN_STATUS_SERIALIZATION_VERSION_MISMATCH = 1003, /**< Serialization version mismatch. @since cuDNN 9.0.0 */
    CUDNN_STATUS_DEPRECATED                     = 1004, /**< Deprecated feature was used. @since cuDNN 9.0.0 */
    CUDNN_STATUS_LICENSE_ERROR                  = 1005, /**< License error. @since cuDNN 9.0.0 */
    CUDNN_STATUS_RUNTIME_IN_PROGRESS            = 1006, /**< Runtime operation in progress. @since cuDNN 9.0.0 */
    CUDNN_STATUS_RUNTIME_FP_OVERFLOW            = 1007, /**< Floating-point overflow at runtime. @since cuDNN 9.0.0 */
    CUDNN_STATUS_SUBLIBRARY_LOADING_FAILED      = 1008, /**< Sub-library loading failed. @since cuDNN 9.2.0 */

    CUDNN_STATUS_BAD_PARAM                     = 2000, /**< Invalid parameter value. @since cuDNN 9.0.0 */
    CUDNN_STATUS_BAD_PARAM_NULL_POINTER        = 2002, /**< Null pointer passed as parameter. @since cuDNN 9.0.0 */
    CUDNN_STATUS_BAD_PARAM_MISALIGNED_POINTER  = 2003, /**< Misaligned pointer passed. @since cuDNN 9.0.0 */
    CUDNN_STATUS_BAD_PARAM_NOT_FINALIZED       = 2004, /**< Descriptor not finalized. @since cuDNN 9.0.0 */
    CUDNN_STATUS_BAD_PARAM_OUT_OF_BOUND        = 2005, /**< Parameter out of bounds. @since cuDNN 9.0.0 */
    CUDNN_STATUS_BAD_PARAM_SIZE_INSUFFICIENT   = 2006, /**< Insufficient buffer size. @since cuDNN 9.0.0 */
    CUDNN_STATUS_BAD_PARAM_STREAM_MISMATCH     = 2007, /**< CUDA stream mismatch. @since cuDNN 9.0.0 */
    CUDNN_STATUS_BAD_PARAM_SHAPE_MISMATCH      = 2008, /**< Tensor shape mismatch. @since cuDNN 9.0.0 */
    CUDNN_STATUS_BAD_PARAM_DUPLICATED_ENTRIES  = 2009, /**< Duplicated entries detected. @since cuDNN 9.0.0 */
    CUDNN_STATUS_BAD_PARAM_ATTRIBUTE_TYPE      = 2010, /**< Wrong attribute type. @since cuDNN 9.0.0 */
    CUDNN_STATUS_BAD_PARAM_CUDA_GRAPH_MISMATCH = 2011, /**< CUDA graph mismatch. @since cuDNN 9.5.0 */
    CUDNN_STATUS_BAD_PARAM_DESCRIPTOR_TYPE     = 2012, /**< Wrong descriptor type. @since cuDNN 9.6.0 */

    CUDNN_STATUS_NOT_SUPPORTED                              = 3000, /**< Operation not supported. @since cuDNN 9.0.0 */
    CUDNN_STATUS_NOT_SUPPORTED_GRAPH_PATTERN                = 3001, /**< Graph pattern not supported. @since cuDNN 9.0.0 */
    CUDNN_STATUS_NOT_SUPPORTED_SHAPE                        = 3002, /**< Shape not supported. @since cuDNN 9.0.0 */
    CUDNN_STATUS_NOT_SUPPORTED_DATA_TYPE                    = 3003, /**< Data type not supported. @since cuDNN 9.0.0 */
    CUDNN_STATUS_NOT_SUPPORTED_LAYOUT                       = 3004, /**< Layout not supported. @since cuDNN 9.0.0 */
    CUDNN_STATUS_NOT_SUPPORTED_INCOMPATIBLE_CUDA_DRIVER     = 3005, /**< Incompatible CUDA driver. @since cuDNN 9.0.0 */
    CUDNN_STATUS_NOT_SUPPORTED_INCOMPATIBLE_CUDART          = 3006, /**< Incompatible CUDA runtime. @since cuDNN 9.0.0 */
    CUDNN_STATUS_NOT_SUPPORTED_ARCH_MISMATCH                = 3007, /**< GPU architecture mismatch. @since cuDNN 9.0.0 */
    CUDNN_STATUS_NOT_SUPPORTED_RUNTIME_PREREQUISITE_MISSING = 3008, /**< Runtime prerequisite missing. @since cuDNN 9.0.0 */
    CUDNN_STATUS_NOT_SUPPORTED_SUBLIBRARY_UNAVAILABLE       = 3009, /**< Sub-library unavailable. @since cuDNN 9.0.0 */
    CUDNN_STATUS_NOT_SUPPORTED_SHARED_MEMORY_INSUFFICIENT   = 3010, /**< Insufficient shared memory. @since cuDNN 9.0.0 */
    CUDNN_STATUS_NOT_SUPPORTED_PADDING                      = 3011, /**< Padding mode not supported. @since cuDNN 9.0.0 */
    CUDNN_STATUS_NOT_SUPPORTED_BAD_LAUNCH_PARAM             = 3012, /**< Bad launch parameters. @since cuDNN 9.0.0 */
    CUDNN_STATUS_NOT_SUPPORTED_CUDA_GRAPH_NATIVE_API        = 3013, /**< CUDA graph native API not supported. @since cuDNN 9.5.0 */
    CUDNN_STATUS_NOT_SUPPORTED_INVALID_DYNAMIC_SHAPE        = 3014, /**< Invalid dynamic shape. @since cuDNN 9.18.0 */

    CUDNN_STATUS_INTERNAL_ERROR                          = 4000, /**< Internal error. @since cuDNN 9.0.0 */
    CUDNN_STATUS_INTERNAL_ERROR_COMPILATION_FAILED       = 4001, /**< Kernel compilation failed. @since cuDNN 9.0.0 */
    CUDNN_STATUS_INTERNAL_ERROR_UNEXPECTED_VALUE         = 4002, /**< Unexpected internal value. @since cuDNN 9.0.0 */
    CUDNN_STATUS_INTERNAL_ERROR_HOST_ALLOCATION_FAILED   = 4003, /**< Host memory allocation failed. @since cuDNN 9.0.0 */
    CUDNN_STATUS_INTERNAL_ERROR_DEVICE_ALLOCATION_FAILED = 4004, /**< Device memory allocation failed. @since cuDNN 9.0.0 */
    CUDNN_STATUS_INTERNAL_ERROR_BAD_LAUNCH_PARAM         = 4005, /**< Bad internal launch parameters. @since cuDNN 9.0.0 */
    CUDNN_STATUS_INTERNAL_ERROR_TEXTURE_CREATION_FAILED  = 4006, /**< Texture creation failed. @since cuDNN 9.0.0 */

    CUDNN_STATUS_EXECUTION_FAILED             = 5000, /**< Execution failed. @since cuDNN 9.0.0 */
    CUDNN_STATUS_EXECUTION_FAILED_CUDA_DRIVER = 5001, /**< CUDA driver execution failure. @since cuDNN 9.0.0 */
    CUDNN_STATUS_EXECUTION_FAILED_CUBLAS      = 5002, /**< cuBLAS execution failure. @since cuDNN 9.0.0 */
    CUDNN_STATUS_EXECUTION_FAILED_CUDART      = 5003, /**< CUDA runtime execution failure. @since cuDNN 9.0.0 */
    CUDNN_STATUS_EXECUTION_FAILED_CURAND      = 5004, /**< cuRAND execution failure. @since cuDNN 9.0.0 */

    CUDNN_STATUS_ALLOC_FAILED CUDNN_DEPRECATED_ENUM  = CUDNN_STATUS_INTERNAL_ERROR_HOST_ALLOCATION_FAILED, /**< @deprecated Use CUDNN_STATUS_INTERNAL_ERROR_HOST_ALLOCATION_FAILED. @since cuDNN 9.0.0 */
    CUDNN_STATUS_INVALID_VALUE CUDNN_DEPRECATED_ENUM = 2001 /* please transition to CUDNN_STATUS_BAD_PARAM instead */, /**< @deprecated Use CUDNN_STATUS_BAD_PARAM. @since cuDNN 9.0.0 */
    CUDNN_STATUS_ARCH_MISMATCH CUDNN_DEPRECATED_ENUM = CUDNN_STATUS_NOT_SUPPORTED_ARCH_MISMATCH, /**< @deprecated Use CUDNN_STATUS_NOT_SUPPORTED_ARCH_MISMATCH. @since cuDNN 9.0.0 */
    CUDNN_STATUS_MAPPING_ERROR CUDNN_DEPRECATED_ENUM = CUDNN_STATUS_INTERNAL_ERROR_TEXTURE_CREATION_FAILED, /**< @deprecated Use CUDNN_STATUS_INTERNAL_ERROR_TEXTURE_CREATION_FAILED. @since cuDNN 9.0.0 */
    CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING CUDNN_DEPRECATED_ENUM =
        CUDNN_STATUS_NOT_SUPPORTED_RUNTIME_PREREQUISITE_MISSING, /**< @deprecated Use CUDNN_STATUS_NOT_SUPPORTED_RUNTIME_PREREQUISITE_MISSING. @since cuDNN 9.0.0 */
    CUDNN_STATUS_VERSION_MISMATCH CUDNN_DEPRECATED_ENUM = CUDNN_STATUS_SUBLIBRARY_VERSION_MISMATCH, /**< @deprecated Use CUDNN_STATUS_SUBLIBRARY_VERSION_MISMATCH. @since cuDNN 9.0.0 */
} cudnnStatus_t;

#define CUDNN_STATUS_FULL_ERROR_CODE(category, specific_err) ((cudnnStatus_t)(0 + (category) + (specific_err)))
#define CUDNN_STATUS_CATEGORY(full_error_code) ((full_error_code) / 1000 * 1000)
#define CUDNN_STATUS_SPECIFIC_ERROR(full_error_code) ((full_error_code) % 1000)

/** @brief Converts status code to human-readable string.
 *  @param[in] status The cuDNN status code to convert.
 *  @return Pointer to a static string describing the status code.
 *  @since cuDNN 9.0.0
 */
/* human-readable error messages */
const char *CUDNNWINAPI
cudnnGetErrorString(cudnnStatus_t status);

/** @brief Retrieves most recent error message. Thread-safe.
 *  @param[out] message   Buffer to receive the error message string.
 *  @param[in]  max_size  Maximum number of bytes to write into @p message.
 *  @since cuDNN 9.0.0
 */
void CUDNNWINAPI
cudnnGetLastErrorString(char *message, size_t max_size);

/* Forward definition in this version only */
typedef struct cudnnRuntimeTag_t cudnnRuntimeTag_t CUDNN_DEPRECATED;

/** @brief Error query modes for cudnnQueryRuntimeError.
 *  @deprecated
 *  @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_ERRQUERY_RAWCODE     = 0, /**< Return raw error code. @since cuDNN 9.0.0 */
    CUDNN_ERRQUERY_NONBLOCKING = 1, /**< Non-blocking error query. @since cuDNN 9.0.0 */
    CUDNN_ERRQUERY_BLOCKING    = 2, /**< Blocking error query. @since cuDNN 9.0.0 */
} cudnnErrQueryMode_t;

/** @brief Queries remote kernel error state.
 *  @deprecated Use cudnnGetLastErrorString instead.
 *  @param[in]  handle   cuDNN handle.
 *  @param[out] rstatus  Pointer to receive the runtime status.
 *  @param[in]  mode     Error query mode.
 *  @param[out] tag      Runtime tag (unused, may be NULL).
 *  @return cuDNN status code.
 *  @since cuDNN 9.0.0
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnQueryRuntimeError(cudnnHandle_t handle, cudnnStatus_t *rstatus, cudnnErrQueryMode_t mode, cudnnRuntimeTag_t *tag);

/** @brief Queries library property (major, minor, or patch version).
 *  @param[in]  type   The property type to query (MAJOR_VERSION, MINOR_VERSION, or PATCH_LEVEL).
 *  @param[out] value  Pointer to receive the property value.
 *  @return cuDNN status code.
 *  @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnGetProperty(libraryPropertyType type, int *value);

/** @brief Creates cuDNN context. Must precede all other cuDNN library calls.
 *  @param[out] handle  Pointer to receive the newly created cuDNN handle.
 *  @retval CUDNN_STATUS_SUCCESS
 *  @retval CUDNN_STATUS_BAD_PARAM
 *  @retval CUDNN_STATUS_NOT_INITIALIZED
 *  @retval CUDNN_STATUS_NOT_SUPPORTED_ARCH_MISMATCH
 *  @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnCreate(cudnnHandle_t *handle);

/** @brief Destroys cuDNN context. Calls cudaDeviceSynchronize.
 *  @param[in] handle  The cuDNN handle to destroy.
 *  @return cuDNN status code.
 *  @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnDestroy(cudnnHandle_t handle);

/** @brief Associates CUDA stream with cuDNN handle.
 *  @param[in] handle    cuDNN handle.
 *  @param[in] streamId  CUDA stream to associate.
 *  @retval CUDNN_STATUS_SUCCESS
 *  @retval CUDNN_STATUS_BAD_PARAM
 *  @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnSetStream(cudnnHandle_t handle, cudaStream_t streamId);

/** @brief Retrieves CUDA stream from cuDNN handle.
 *  @param[in]  handle    cuDNN handle.
 *  @param[out] streamId  Pointer to receive the associated CUDA stream.
 *  @retval CUDNN_STATUS_SUCCESS
 *  @retval CUDNN_STATUS_BAD_PARAM
 *  @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnGetStream(cudnnHandle_t handle, cudaStream_t *streamId);
/** @brief Supported data types for cuDNN tensors and operations.
 *  @since cuDNN 9.0.0
 */
/*
 * CUDNN data type
 */
typedef enum {
    CUDNN_DATA_FLOAT                         = 0,  /**< 32-bit IEEE floating point. @since cuDNN 9.0.0 */
    CUDNN_DATA_DOUBLE                        = 1,  /**< 64-bit IEEE floating point. @since cuDNN 9.0.0 */
    CUDNN_DATA_HALF                          = 2,  /**< 16-bit IEEE floating point (FP16). @since cuDNN 9.0.0 */
    CUDNN_DATA_INT8                          = 3,  /**< 8-bit signed integer. @since cuDNN 9.0.0 */
    CUDNN_DATA_INT32                         = 4,  /**< 32-bit signed integer. @since cuDNN 9.0.0 */
    CUDNN_DATA_INT8x4 CUDNN_DEPRECATED_ENUM  = 5,  /**< @deprecated Vectorized 4x INT8. @since cuDNN 9.0.0 */
    CUDNN_DATA_UINT8                         = 6,  /**< 8-bit unsigned integer. @since cuDNN 9.0.0 */
    CUDNN_DATA_UINT8x4 CUDNN_DEPRECATED_ENUM = 7,  /**< @deprecated Vectorized 4x UINT8. @since cuDNN 9.0.0 */
    CUDNN_DATA_INT8x32 CUDNN_DEPRECATED_ENUM = 8,  /**< @deprecated Vectorized 32x INT8. @since cuDNN 9.0.0 */
    CUDNN_DATA_BFLOAT16                      = 9,  /**< Brain floating point (BF16). @since cuDNN 9.0.0 */
    CUDNN_DATA_INT64                         = 10, /**< 64-bit signed integer. @since cuDNN 9.0.0 */
    CUDNN_DATA_BOOLEAN                       = 11, /**< Boolean type. @since cuDNN 9.0.0 */
    CUDNN_DATA_FP8_E4M3                      = 12, /**< FP8 with 4-bit exponent, 3-bit mantissa. @since cuDNN 9.0.0 */
    CUDNN_DATA_FP8_E5M2                      = 13, /**< FP8 with 5-bit exponent, 2-bit mantissa. @since cuDNN 9.0.0 */
    CUDNN_DATA_FAST_FLOAT_FOR_FP8            = 14, /**< Fast float accumulator type for FP8 compute paths. @since cuDNN 9.0.0 */
    CUDNN_DATA_FP8_E8M0                      = 15, /**< Pure-exponent scale format (8-bit exponent, 0-bit mantissa) for block scaling. @since cuDNN 9.7.0 */
    CUDNN_DATA_FP4_E2M1                      = 16, /**< FP4 with 2-bit exponent, 1-bit mantissa. @since cuDNN 9.7.0 */
    CUDNN_DATA_INT4                          = 17, /**< 4-bit signed integer. @since cuDNN 9.11.0 */
    CUDNN_DATA_UINT4                         = 18, /**< 4-bit unsigned integer. @since cuDNN 9.11.0 */
    CUDNN_DATA_UINT32                        = 19, /**< 32-bit unsigned integer. @since cuDNN 9.11.0 */
    CUDNN_DATA_COMPLEX_FP32                  = 20, /**< Complex 32-bit floating point. @since cuDNN 9.14.0 */
    CUDNN_DATA_COMPLEX_FP64                  = 21, /**< Complex 64-bit floating point. @since cuDNN 9.14.0 */
    CUDNN_DATA_BYTE_BOOLEAN                  = 22, /**< Byte-addressed boolean type. @since cuDNN 9.25.0 */
} cudnnDataType_t;

/** @brief Math precision modes for cuDNN operations.
 *  @since cuDNN 9.0.0
 */
/*
 * CUDNN math type
 */
typedef enum {
    CUDNN_DEFAULT_MATH                    = 0, /**< Default math mode. @since cuDNN 9.0.0 */
    CUDNN_TENSOR_OP_MATH                  = 1, /**< Tensor Core math. @since cuDNN 9.0.0 */
    CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION = 2, /**< Tensor Core math with type conversion. @since cuDNN 9.0.0 */
    CUDNN_FMA_MATH                        = 3, /**< FMA (fused multiply-add) math only. @since cuDNN 9.0.0 */
} cudnnMathType_t;

/** @brief NaN propagation modes.
 *  @deprecated
 *  @since cuDNN 9.0.0
 */
/*
 * CUDNN propagate Nan
 */
typedef enum {
    CUDNN_NOT_PROPAGATE_NAN CUDNN_DEPRECATED_ENUM = 0, /**< @deprecated Do not propagate NaN. @since cuDNN 9.0.0 */
    CUDNN_PROPAGATE_NAN CUDNN_DEPRECATED_ENUM     = 1, /**< @deprecated Propagate NaN values. @since cuDNN 9.0.0 */
} cudnnNanPropagation_t;

/** @brief CTC gradient modes controlling behavior for out-of-bounds (OOB) samples.
 *  @since cuDNN 9.0.0
 */
/*
 * Behavior for OOB samples. OOB samples are samples where L+R > T is encountered during the gradient calculation. If
 * gradMode is set to CUDNN_CTC_SKIP_OOB_GRADIENTS, then the CTC loss function does not write to the gradient buffer for
 * that sample. Instead, the current values, even not finite, are retained. If gradMode is set to
 * CUDNN_CTC_ZERO_OOB_GRADIENTS, then the gradient for that sample is set to zero. This guarantees a finite gradient.
 */
typedef enum {
    CUDNN_CTC_ZERO_OOB_GRADIENTS = 0, /**< Zero the gradient for OOB samples (guarantees finite). @since cuDNN 9.0.0 */
    CUDNN_CTC_SKIP_OOB_GRADIENTS = 1, /**< Skip writing gradient for OOB samples. @since cuDNN 9.0.0 */
} cudnnCTCGradMode_t;

/** @brief Tensor memory layout formats.
 *  @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_TENSOR_NCHW        = 0, /**< Row major layout (wStride = 1, hStride = w). @since cuDNN 9.0.0 */
    CUDNN_TENSOR_NHWC        = 1, /**< Feature maps interleaved (cStride = 1). @since cuDNN 9.0.0 */
    CUDNN_TENSOR_NCHW_VECT_C = 2, /**< Vectorized channel layout, vector length in data type. @since cuDNN 9.0.0 */
} cudnnTensorFormat_t;

/** @brief Reduction operations for tensor reduction.
 *  @since cuDNN 9.0.0
 */
/*
 * CUDNN ReduceTensor op type
 */
typedef enum {
    CUDNN_REDUCE_TENSOR_ADD          = 0, /**< Sum reduction. @since cuDNN 9.0.0 */
    CUDNN_REDUCE_TENSOR_MUL          = 1, /**< Product reduction. @since cuDNN 9.0.0 */
    CUDNN_REDUCE_TENSOR_MIN          = 2, /**< Minimum value reduction. @since cuDNN 9.0.0 */
    CUDNN_REDUCE_TENSOR_MAX          = 3, /**< Maximum value reduction. @since cuDNN 9.0.0 */
    CUDNN_REDUCE_TENSOR_AMAX         = 4, /**< Maximum absolute value reduction. @since cuDNN 9.0.0 */
    CUDNN_REDUCE_TENSOR_AVG          = 5, /**< Average reduction. @since cuDNN 9.0.0 */
    CUDNN_REDUCE_TENSOR_NORM1        = 6, /**< L1 norm reduction. @since cuDNN 9.0.0 */
    CUDNN_REDUCE_TENSOR_NORM2        = 7, /**< L2 norm reduction. @since cuDNN 9.0.0 */
    CUDNN_REDUCE_TENSOR_MUL_NO_ZEROS = 8, /**< Product reduction ignoring zeros. @since cuDNN 9.0.0 */
} cudnnReduceTensorOp_t;

/** @brief Activation function modes.
 *  @deprecated Use pointwise operations instead.
 *  @since cuDNN 9.0.0
 */
/*
 * activation mode
 */
typedef enum {
    CUDNN_ACTIVATION_SIGMOID      = 0, /**< @deprecated Sigmoid activation. @since cuDNN 9.0.0 */
    CUDNN_ACTIVATION_RELU         = 1, /**< @deprecated ReLU activation. @since cuDNN 9.0.0 */
    CUDNN_ACTIVATION_TANH         = 2, /**< @deprecated Tanh activation. @since cuDNN 9.0.0 */
    CUDNN_ACTIVATION_CLIPPED_RELU = 3, /**< @deprecated Clipped ReLU activation. @since cuDNN 9.0.0 */
    CUDNN_ACTIVATION_ELU          = 4, /**< @deprecated ELU activation. @since cuDNN 9.0.0 */
    CUDNN_ACTIVATION_IDENTITY     = 5, /**< @deprecated Identity (pass-through). @since cuDNN 9.0.0 */
    CUDNN_ACTIVATION_SWISH        = 6  /**< @deprecated Swish activation. @since cuDNN 9.0.0 */
} cudnnActivationMode_t CUDNN_DEPRECATED;

/** @brief Debug severity levels for cuDNN callback messages.
 *  @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_SEV_FATAL   = 0, /**< Fatal error severity. @since cuDNN 9.0.0 */
    CUDNN_SEV_ERROR   = 1, /**< Error severity. @since cuDNN 9.0.0 */
    CUDNN_SEV_WARNING = 2, /**< Warning severity. @since cuDNN 9.0.0 */
    CUDNN_SEV_INFO    = 3, /**< Informational severity. @since cuDNN 9.0.0 */
} cudnnSeverity_t;

/* Message masks to be used with cudnnSetCallback() */
#define CUDNN_SEV_ERROR_EN (1U << CUDNN_SEV_ERROR)
#define CUDNN_SEV_WARNING_EN (1U << CUDNN_SEV_WARNING)
#define CUDNN_SEV_INFO_EN (1U << CUDNN_SEV_INFO)

/** @brief Debug callback metadata containing version, status, timestamps, handle, stream, PID, TID, and device ID.
 *  @since cuDNN 9.0.0
 */
/* struct containing useful informaiton for each API call */
typedef struct cudnnDebugStruct {
    unsigned cudnn_version;      /**< cuDNN library version. */
    cudnnStatus_t cudnnStatus;   /**< Status code for this API call. */
    unsigned time_sec;           /**< Epoch time in seconds. */
    unsigned time_usec;          /**< Microseconds part of epoch time. */
    unsigned time_delta;         /**< Time since start in seconds. */
    cudnnHandle_t handle;        /**< cuDNN handle. */
    cudaStream_t stream;         /**< CUDA stream ID. */
    unsigned long long pid;      /**< Process ID. */
    unsigned long long tid;      /**< Thread ID. */
    int cudaDeviceId;            /**< CUDA device ID. */
    int reserved[15];            /**< Reserved for future use. */
} cudnnDebug_t;

/** @brief Callback function type for cuDNN debug messages.
 *  @since cuDNN 9.0.0
 */
typedef void (*cudnnCallback_t)(cudnnSeverity_t sev, void *udata, const cudnnDebug_t *dbg, const char *msg);

/** @brief Registers debug callback with message mask.
 *  @param[in] mask   Bitmask of severity levels to enable (see CUDNN_SEV_*_EN).
 *  @param[in] udata  User data pointer passed to callback.
 *  @param[in] fptr   Callback function pointer.
 *  @return cuDNN status code.
 *  @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnSetCallback(unsigned mask, void *udata, cudnnCallback_t fptr);

/** @brief Retrieves registered debug callback and its configuration.
 *  @param[out] mask   Pointer to receive the current severity mask.
 *  @param[out] udata  Pointer to receive the user data pointer.
 *  @param[out] fptr   Pointer to receive the callback function pointer.
 *  @return cuDNN status code.
 *  @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnGetCallback(unsigned *mask, void **udata, cudnnCallback_t *fptr);

/** @brief Cross-library version checker.
 *
 *  This function is implemented differently in each sub-library. Each sublib
 *  checks whether its own version matches that of its dependencies.
 *  @retval CUDNN_STATUS_SUCCESS if the version check passes.
 *  @retval CUDNN_STATUS_SUBLIBRARY_VERSION_MISMATCH if the versions are inconsistent.
 *  @since cuDNN 9.0.0
 */
/*
 * \brief Cross-library version checker.
 * This function is implemented differently in each sub-library. Each sublib
 * checks whether its own version matches that of its dependencies.
 * \returns CUDNN_STATUS_SUCCESS if the version check passes,
 *          CUDNN_STATUS_SUBLIBRARY_VERSION_MISMATCH if the versions are inconsistent.
 */
cudnnStatus_t CUDNNWINAPI
cudnnGraphVersionCheck(void);

/* Maximum supported number of tensor dimensions */
#define CUDNN_DIM_MAX 8

/** @brief Convolution operation modes.
 *  @since cuDNN 9.0.0
 */
/*
 *  convolution mode
 */
typedef enum { CUDNN_CONVOLUTION = 0, /**< Standard convolution. @since cuDNN 9.0.0 */ CUDNN_CROSS_CORRELATION = 1 /**< Cross-correlation. @since cuDNN 9.0.0 */ } cudnnConvolutionMode_t;

/** @brief Tensor reorder type.
 *  @deprecated
 *  @since cuDNN 9.0.0
 */
/*
 * CUDNN Reorder
 */
typedef enum {
    CUDNN_DEFAULT_REORDER = 0, /**< @deprecated Default reordering behavior. @since cuDNN 9.0.0 */
    CUDNN_NO_REORDER      = 1, /**< @deprecated No reordering. @since cuDNN 9.0.0 */
} cudnnReorderType_t CUDNN_DEPRECATED;

/** @brief Opaque pointer to a cuDNN backend descriptor.
 *  @since cuDNN 9.0.0
 */
typedef void *cudnnBackendDescriptor_t;

/** @brief Integer fraction with numerator and denominator.
 *  @since cuDNN 9.0.0
 */
typedef struct cudnnFractionStruct {
    int64_t numerator;   /**< Fraction numerator. */
    int64_t denominator; /**< Fraction denominator. */
} cudnnFraction_t;

/** @brief Pointwise operation modes including binary, unary, activation forward/backward,
 *  comparison, logical, and special operations.
 *  @since cuDNN 9.0.0
 */
typedef enum {
    /* Binary operations (0-9) */
    CUDNN_POINTWISE_ADD        = 0,  /**< Element-wise addition. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_ADD_SQUARE = 5,  /**< Element-wise add-and-square. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_DIV        = 6,  /**< Element-wise division. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_MAX        = 3,  /**< Element-wise maximum. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_MIN        = 2,  /**< Element-wise minimum. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_MOD        = 7,  /**< Element-wise modulo. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_MUL        = 1,  /**< Element-wise multiplication. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_POW        = 8,  /**< Element-wise power. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_SUB        = 9,  /**< Element-wise subtraction. @since cuDNN 9.0.0 */

    /* Unary operations (10-23) */
    CUDNN_POINTWISE_ABS        = 10, /**< Absolute value. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_CEIL       = 11, /**< Ceiling. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_COS        = 12, /**< Cosine. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_EXP        = 13, /**< Exponential. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_FLOOR      = 14, /**< Floor. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_LOG        = 15, /**< Natural logarithm. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_NEG        = 16, /**< Negation. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_RSQRT      = 17, /**< Reciprocal square root. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_SIN        = 18, /**< Sine. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_SQRT       = 4,  /**< Square root. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_TAN        = 19, /**< Tangent. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_ERF        = 20, /**< Error function. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_IDENTITY   = 21, /**< Identity (no-op); enables implicit data type conversion between tensors. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_RECIPROCAL = 22, /**< Reciprocal. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_ATAN2      = 23, /**< Two-argument arctangent. @since cuDNN 9.1.0 */

    /* Activation forward (100-107) */
    CUDNN_POINTWISE_RELU_FWD             = 100, /**< ReLU forward. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_TANH_FWD             = 101, /**< Tanh forward. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_SIGMOID_FWD          = 102, /**< Sigmoid forward. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_ELU_FWD              = 103, /**< ELU forward. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_GELU_FWD             = 104, /**< GELU forward. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_SOFTPLUS_FWD         = 105, /**< Softplus forward. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_SWISH_FWD            = 106, /**< Swish forward. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_GELU_APPROX_TANH_FWD = 107, /**< GELU forward using tanh approximation: 0.5*x*(1+tanh[sqrt(2/pi)*(x+0.044715*x^3)]). @since cuDNN 9.0.0 */

    /* Activation backward (200-207) */
    CUDNN_POINTWISE_RELU_BWD             = 200, /**< ReLU backward. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_TANH_BWD             = 201, /**< Tanh backward. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_SIGMOID_BWD          = 202, /**< Sigmoid backward. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_ELU_BWD              = 203, /**< ELU backward. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_GELU_BWD             = 204, /**< GELU backward. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_SOFTPLUS_BWD         = 205, /**< Softplus backward. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_SWISH_BWD            = 206, /**< Swish backward. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_GELU_APPROX_TANH_BWD = 207, /**< GELU backward using tanh approximation. @since cuDNN 9.0.0 */

    /* Comparison operations (300-305) */
    CUDNN_POINTWISE_CMP_EQ  = 300, /**< Equal comparison. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_CMP_NEQ = 301, /**< Not-equal comparison. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_CMP_GT  = 302, /**< Greater-than comparison. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_CMP_GE  = 303, /**< Greater-or-equal comparison. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_CMP_LT  = 304, /**< Less-than comparison. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_CMP_LE  = 305, /**< Less-or-equal comparison. @since cuDNN 9.0.0 */

    /* Logical operations (400-402) */
    CUDNN_POINTWISE_LOGICAL_AND = 400, /**< Logical AND. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_LOGICAL_OR  = 401, /**< Logical OR. @since cuDNN 9.0.0 */
    CUDNN_POINTWISE_LOGICAL_NOT = 402, /**< Logical NOT. @since cuDNN 9.0.0 */

    /* Special operations (500+) */
    CUDNN_POINTWISE_GEN_INDEX = 501, /**< Generates a tensor of index values along a given axis. @since cuDNN 9.0.0 */

    CUDNN_POINTWISE_BINARY_SELECT = 601, /**< Ternary select: y = predicate ? x : b, using three input tensors. @since cuDNN 9.0.0 */
} cudnnPointwiseMode_t;

/** @brief Resampling modes for pooling and interpolation.
 *  @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_RESAMPLE_NEAREST                 = 0, /**< Nearest-neighbor interpolation. @since cuDNN 9.0.0 */
    CUDNN_RESAMPLE_BILINEAR                = 1, /**< Bilinear interpolation. @since cuDNN 9.0.0 */
    CUDNN_RESAMPLE_AVGPOOL                 = 2, /**< Average pooling (include padding). @since cuDNN 9.0.0 */
    CUDNN_RESAMPLE_AVGPOOL_INCLUDE_PADDING = 2, /**< Average pooling including padding in divisor. @since cuDNN 9.0.0 */
    CUDNN_RESAMPLE_AVGPOOL_EXCLUDE_PADDING = 4, /**< Average pooling excluding padding from divisor. @since cuDNN 9.0.0 */
    CUDNN_RESAMPLE_MAXPOOL                 = 3, /**< Max pooling. @since cuDNN 9.0.0 */
} cudnnResampleMode_t;

/** @brief Signal synchronization modes.
 *  @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_SIGNAL_SET  = 0, /**< Set signal. @since cuDNN 9.0.0 */
    CUDNN_SIGNAL_WAIT = 1, /**< Wait on signal. @since cuDNN 9.0.0 */
} cudnnSignalMode_t;

/** @brief Statistics generation mode.
 *  @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_GENSTATS_SUM_SQSUM = 0, /**< Generate sum and sum-of-squares statistics. @since cuDNN 9.0.0 */
} cudnnGenStatsMode_t;

/** @brief Batch normalization finalize statistics mode.
 *  @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_BN_FINALIZE_STATISTICS_TRAINING  = 0, /**< Training mode finalization. @since cuDNN 9.0.0 */
    CUDNN_BN_FINALIZE_STATISTICS_INFERENCE = 1, /**< Inference mode finalization. @since cuDNN 9.0.0 */
} cudnnBnFinalizeStatsMode_t;

/** @brief Random number generator distribution types.
 *  @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_RNG_DISTRIBUTION_BERNOULLI = 0, /**< Bernoulli distribution. @since cuDNN 9.0.0 */
    CUDNN_RNG_DISTRIBUTION_UNIFORM   = 1, /**< Uniform distribution. @since cuDNN 9.0.0 */
    CUDNN_RNG_DISTRIBUTION_NORMAL    = 2, /**< Normal (Gaussian) distribution. @since cuDNN 9.0.0 */
} cudnnRngDistribution_t;

/** @brief Mixture-of-Experts grouped matmul modes.
 *  @since cuDNN 9.15.0
 */
typedef enum {
    CUDNN_MOE_GROUPED_MATMUL_MODE_NONE    = 0, /**< No gather/scatter. @since cuDNN 9.15.0 */
    CUDNN_MOE_GROUPED_MATMUL_MODE_GATHER  = 1, /**< Gather mode. @since cuDNN 9.15.0 */
    CUDNN_MOE_GROUPED_MATMUL_MODE_SCATTER = 2, /**< Scatter mode. @since cuDNN 9.15.0 */
} cudnnMoeGroupedMatmulMode_t;

/** @brief Backend attribute names for configuring and querying backend descriptors.
 *
 *  Attribute names are grouped by descriptor type and numeric range:
 *  - 0-9: Pointwise attributes
 *  - 100-106: Convolution attributes
 *  - 200-204: Engine heuristic attributes
 *  - 300-304: Engine config attributes
 *  - 400-407: Execution plan attributes
 *  - 500-503: Intermediate info attributes
 *  - 600-601: Knob choice attributes
 *  - 700-717: Operation convolution attributes
 *  - 750-758: Operation pointwise attributes
 *  - 770-796: Operation gen-stats and BN finalize attributes
 *  - 800-804: Operation graph attributes
 *  - 900-913: Tensor attributes
 *  - 1000-1012: Variant pack attributes
 *  - 1100+: Layout info, knob info, engine, matmul, reduction, resample, etc.
 *  @since cuDNN 9.0.0
 */
typedef enum {
    /* Pointwise attributes */
    CUDNN_ATTR_POINTWISE_MODE                                  = 0, /**< Pointwise operation type. @since cuDNN 9.0.0 */
    CUDNN_ATTR_POINTWISE_MATH_PREC                             = 1, /**< Computation precision for pointwise ops. @since cuDNN 9.0.0 */
    CUDNN_ATTR_POINTWISE_NAN_PROPAGATION CUDNN_DEPRECATED_ENUM = 2, /**< NaN handling behavior. @deprecated @since cuDNN 9.0.0 */
    CUDNN_ATTR_POINTWISE_RELU_LOWER_CLIP                       = 3, /**< Lower clipping threshold for ReLU. @since cuDNN 9.0.0 */
    CUDNN_ATTR_POINTWISE_RELU_UPPER_CLIP                       = 4, /**< Upper clipping threshold for ReLU. @since cuDNN 9.0.0 */
    CUDNN_ATTR_POINTWISE_RELU_LOWER_CLIP_SLOPE                 = 5, /**< Slope below lower clip for leaky ReLU. @since cuDNN 9.0.0 */
    CUDNN_ATTR_POINTWISE_ELU_ALPHA                             = 6, /**< Alpha parameter for ELU activation. @since cuDNN 9.0.0 */
    CUDNN_ATTR_POINTWISE_SOFTPLUS_BETA                         = 7, /**< Beta parameter for softplus function. @since cuDNN 9.0.0 */
    CUDNN_ATTR_POINTWISE_SWISH_BETA                            = 8, /**< Beta parameter for swish activation. @since cuDNN 9.0.0 */
    CUDNN_ATTR_POINTWISE_AXIS                                  = 9, /**< Axis for axis-dependent pointwise operations. @since cuDNN 9.0.0 */

    /* Convolution attributes */
    CUDNN_ATTR_CONVOLUTION_COMP_TYPE      = 100, /**< Computation data type for convolution. @since cuDNN 9.0.0 */
    CUDNN_ATTR_CONVOLUTION_CONV_MODE      = 101, /**< Convolution vs cross-correlation mode. @since cuDNN 9.0.0 */
    CUDNN_ATTR_CONVOLUTION_DILATIONS      = 102, /**< Dilation factors per spatial dimension. @since cuDNN 9.0.0 */
    CUDNN_ATTR_CONVOLUTION_FILTER_STRIDES = 103, /**< Filter stride values per spatial dimension. @since cuDNN 9.0.0 */
    CUDNN_ATTR_CONVOLUTION_POST_PADDINGS  = 104, /**< Post-paddings per spatial dimension. @since cuDNN 9.0.0 */
    CUDNN_ATTR_CONVOLUTION_PRE_PADDINGS   = 105, /**< Pre-paddings per spatial dimension. @since cuDNN 9.0.0 */
    CUDNN_ATTR_CONVOLUTION_SPATIAL_DIMS   = 106, /**< Number of spatial dimensions. @since cuDNN 9.0.0 */

    /* Engine heuristic attributes */
    CUDNN_ATTR_ENGINEHEUR_MODE            = 200, /**< Heuristic algorithm selection mode. @since cuDNN 9.0.0 */
    CUDNN_ATTR_ENGINEHEUR_OPERATION_GRAPH = 201, /**< Associated operation graph descriptor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_ENGINEHEUR_RESULTS         = 202, /**< Array of resulting engine configurations. @since cuDNN 9.0.0 */
    CUDNN_ATTR_ENGINEHEUR_SM_COUNT_TARGET = 203, /**< Target streaming multiprocessor count. @since cuDNN 9.0.0 */
    CUDNN_ATTR_ENGINEHEUR_DEVICEPROP      = 204, /**< Device properties for heuristic query. @since cuDNN 9.8.0 */

    /* Engine config attributes */
    CUDNN_ATTR_ENGINECFG_ENGINE             = 300, /**< Selected engine from heuristic results. @since cuDNN 9.0.0 */
    CUDNN_ATTR_ENGINECFG_INTERMEDIATE_INFO  = 301, /**< Intermediate tensor information. @since cuDNN 9.0.0 */
    CUDNN_ATTR_ENGINECFG_KNOB_CHOICES       = 302, /**< Performance tuning knob selections. @since cuDNN 9.0.0 */
    CUDNN_ATTR_ENGINECFG_WORKSPACE_SIZE     = 303, /**< Required workspace memory size. @since cuDNN 9.2.0 */
    CUDNN_ATTR_ENGINECFG_SHARED_MEMORY_USED = 304, /**< Shared memory used by engine. @since cuDNN 9.2.0 */

    /* Execution plan attributes */
    CUDNN_ATTR_EXECUTION_PLAN_HANDLE CUDNN_DEPRECATED_ENUM = 400, /**< Associated cuDNN handle. @deprecated @since cuDNN 9.0.0 */
    CUDNN_ATTR_EXECUTION_PLAN_ENGINE_CONFIG                = 401, /**< Engine configuration to execute. @since cuDNN 9.0.0 */
    CUDNN_ATTR_EXECUTION_PLAN_WORKSPACE_SIZE               = 402, /**< Total workspace size requirement. @since cuDNN 9.0.0 */
    CUDNN_ATTR_EXECUTION_PLAN_COMPUTED_INTERMEDIATE_UIDS   = 403, /**< UIDs of computed intermediates. @since cuDNN 9.0.0 */
    CUDNN_ATTR_EXECUTION_PLAN_RUN_ONLY_INTERMEDIATE_UIDS   = 404, /**< Run-only intermediate UIDs. @since cuDNN 9.0.0 */
    CUDNN_ATTR_EXECUTION_PLAN_JSON_REPRESENTATION          = 405, /**< Human-readable execution plan JSON. @since cuDNN 9.0.0 */
    CUDNN_ATTR_EXECUTION_PLAN_KERNEL_CACHE                 = 406, /**< Compiled kernel cache descriptor. @since cuDNN 9.4.0 */
    CUDNN_ATTR_EXECUTION_PLAN_DEVICEPROP                   = 407, /**< Device properties for execution plan. @since cuDNN 9.8.0 */

    /* Intermediate info attributes */
    CUDNN_ATTR_INTERMEDIATE_INFO_UNIQUE_ID            = 500, /**< Unique identifier for intermediate tensor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_INTERMEDIATE_INFO_SIZE                 = 501, /**< Memory size requirement in bytes. @since cuDNN 9.0.0 */
    CUDNN_ATTR_INTERMEDIATE_INFO_DEPENDENT_DATA_UIDS  = 502, /**< Data dependency UIDs. @since cuDNN 9.0.0 */
    CUDNN_ATTR_INTERMEDIATE_INFO_DEPENDENT_ATTRIBUTES = 503, /**< Attribute dependencies. @since cuDNN 9.0.0 */

    /* Knob choice attributes */
    CUDNN_ATTR_KNOB_CHOICE_KNOB_TYPE  = 600, /**< Type of performance tuning knob. @since cuDNN 9.0.0 */
    CUDNN_ATTR_KNOB_CHOICE_KNOB_VALUE = 601, /**< Selected value for the knob. @since cuDNN 9.0.0 */

    /* Operation convolution attributes */
    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_ALPHA        = 700, /**< Forward convolution scaling factor alpha. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_BETA         = 701, /**< Forward convolution scaling factor beta. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_CONV_DESC    = 702, /**< Forward convolution descriptor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_W            = 703, /**< Forward convolution filter weight tensor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_X            = 704, /**< Forward convolution input tensor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_Y            = 705, /**< Forward convolution output tensor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_ALPHA       = 706, /**< Backward data scaling factor alpha. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_BETA        = 707, /**< Backward data scaling factor beta. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_CONV_DESC   = 708, /**< Backward data convolution descriptor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_W           = 709, /**< Backward data filter weights. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_DX          = 710, /**< Backward data input gradient output. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_DY          = 711, /**< Backward data output gradient input. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_ALPHA     = 712, /**< Backward filter scaling factor alpha. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_BETA      = 713, /**< Backward filter scaling factor beta. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_CONV_DESC = 714, /**< Backward filter convolution descriptor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_DW        = 715, /**< Backward filter gradient output. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_X         = 716, /**< Backward filter input feature maps. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_DY        = 717, /**< Backward filter output gradients. @since cuDNN 9.0.0 */

    /* Operation pointwise attributes */
    CUDNN_ATTR_OPERATION_POINTWISE_PW_DESCRIPTOR = 750, /**< Pointwise descriptor reference. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_POINTWISE_XDESC         = 751, /**< First input tensor descriptor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_POINTWISE_BDESC         = 752, /**< Bias or second operand descriptor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_POINTWISE_YDESC         = 753, /**< Output tensor descriptor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_POINTWISE_ALPHA1        = 754, /**< First scaling constant. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_POINTWISE_ALPHA2        = 755, /**< Second scaling constant. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_POINTWISE_DXDESC        = 756, /**< Input gradient descriptor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_POINTWISE_DYDESC        = 757, /**< Output gradient descriptor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_POINTWISE_TDESC         = 758, /**< Intermediate tensor descriptor. @since cuDNN 9.0.0 */

    /* Operation gen-stats attributes */
    CUDNN_ATTR_OPERATION_GENSTATS_MODE      = 770, /**< Statistics computation mode. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_GENSTATS_MATH_PREC = 771, /**< Computation precision for statistics. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_GENSTATS_XDESC     = 772, /**< Input data tensor descriptor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_GENSTATS_SUMDESC   = 773, /**< Sum output descriptor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_GENSTATS_SQSUMDESC = 774, /**< Sum of squares output descriptor. @since cuDNN 9.0.0 */

    /* Operation batch normalization finalize attributes */
    CUDNN_ATTR_OPERATION_BN_FINALIZE_STATS_MODE                = 780, /**< Training vs inference mode. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_FINALIZE_MATH_PREC                 = 781, /**< Computation precision. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_FINALIZE_Y_SUM_DESC                = 782, /**< Sum of batch norm outputs. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_FINALIZE_Y_SQ_SUM_DESC             = 783, /**< Sum of squared outputs. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_FINALIZE_SCALE_DESC                = 784, /**< Batch norm scale parameter. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_FINALIZE_BIAS_DESC                 = 785, /**< Batch norm bias parameter. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_FINALIZE_PREV_RUNNING_MEAN_DESC    = 786, /**< Previous running mean. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_FINALIZE_PREV_RUNNING_VAR_DESC     = 787, /**< Previous running variance. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_FINALIZE_UPDATED_RUNNING_MEAN_DESC = 788, /**< Updated running mean output. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_FINALIZE_UPDATED_RUNNING_VAR_DESC  = 789, /**< Updated running variance output. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_FINALIZE_SAVED_MEAN_DESC           = 790, /**< Cached mean for backward pass. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_FINALIZE_SAVED_INV_STD_DESC        = 791, /**< Cached inverse std dev for backward. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_FINALIZE_EQ_SCALE_DESC             = 792, /**< Equivalent scale for fused inference. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_FINALIZE_EQ_BIAS_DESC              = 793, /**< Equivalent bias for fused inference. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_FINALIZE_ACCUM_COUNT_DESC          = 794, /**< Accumulation sample counter. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_FINALIZE_EPSILON_DESC              = 795, /**< Numerical stability epsilon. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_FINALIZE_EXP_AVERATE_FACTOR_DESC   = 796, /**< Exponential averaging factor. @since cuDNN 9.0.0 */

    /* Operation graph attributes */
    CUDNN_ATTR_OPERATIONGRAPH_HANDLE CUDNN_DEPRECATED_ENUM = 800, /**< Associated cuDNN handle. @deprecated @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATIONGRAPH_OPS                          = 801, /**< Array of operation descriptors in the graph. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATIONGRAPH_ENGINE_GLOBAL_COUNT          = 802, /**< Total number of engines available. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATIONGRAPH_IS_DYNAMIC_SHAPE_ENABLED     = 803, /**< Dynamic shape support flag. @since cuDNN 9.4.0 */
    CUDNN_ATTR_OPERATIONGRAPH_IS_SAME_TOPOLOGY             = 804, /**< Same topology reuse flag. @since cuDNN 9.6.0 */
    CUDNN_ATTR_OPERATIONGRAPH_IS_OVERRIDE_SHAPE_ENABLED    = 805, /**< Dynamic shape support with execute time override @since cuDNN 9.21.0 */
    CUDNN_ATTR_OPERATIONGRAPH_MODE                         = 806, /**< Optional cudnnBackendOperationGraphMode_t — see enum doc. @since cuDNN 9.NEXT */

    /* Tensor attributes */
    CUDNN_ATTR_TENSOR_BYTE_ALIGNMENT           = 900, /**< Memory alignment requirement in bytes. @since cuDNN 9.0.0 */
    CUDNN_ATTR_TENSOR_DATA_TYPE                = 901, /**< Element data type. @since cuDNN 9.0.0 */
    CUDNN_ATTR_TENSOR_DIMENSIONS               = 902, /**< Dimension sizes array. @since cuDNN 9.0.0 */
    CUDNN_ATTR_TENSOR_STRIDES                  = 903, /**< Memory strides per dimension. @since cuDNN 9.0.0 */
    CUDNN_ATTR_TENSOR_VECTOR_COUNT             = 904, /**< Vectorization element count. @since cuDNN 9.0.0 */
    CUDNN_ATTR_TENSOR_VECTORIZED_DIMENSION     = 905, /**< Which dimension is vectorized. @since cuDNN 9.0.0 */
    CUDNN_ATTR_TENSOR_UNIQUE_ID                = 906, /**< Unique identifier for graph connectivity. @since cuDNN 9.0.0 */
    CUDNN_ATTR_TENSOR_IS_VIRTUAL               = 907, /**< Virtual (intermediate) vs I/O tensor flag. @since cuDNN 9.0.0 */
    CUDNN_ATTR_TENSOR_IS_BY_VALUE              = 908, /**< Constant scalar vs device pointer flag. @since cuDNN 9.0.0 */
    CUDNN_ATTR_TENSOR_REORDERING_MODE          = 909, /**< Memory layout transformation type. @since cuDNN 9.0.0 */
    CUDNN_ATTR_TENSOR_CONSTANT_VALUE           = 910, /**< Compile-time constant value for by-value tensors. @since cuDNN 9.22.0 */
    CUDNN_ATTR_TENSOR_RAGGED_OFFSET_DESC       = 913, /**< Ragged tensor offset descriptor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_TENSOR_RAGGED_OFFSET_MULTIPLIER = 914, /**< Ragged tensor offset unit size. @since cuDNN 9.24.0 */

    /* Variant pack attributes */
    CUDNN_ATTR_VARIANT_PACK_UNIQUE_IDS          = 1000, /**< Tensor UIDs in this variant pack. @since cuDNN 9.0.0 */
    CUDNN_ATTR_VARIANT_PACK_DATA_POINTERS       = 1001, /**< GPU memory data pointers array. @since cuDNN 9.0.0 */
    CUDNN_ATTR_VARIANT_PACK_INTERMEDIATES       = 1002, /**< Intermediate tensor pointers. @since cuDNN 9.0.0 */
    CUDNN_ATTR_VARIANT_PACK_WORKSPACE           = 1003, /**< Workspace memory pointer. @since cuDNN 9.0.0 */
    CUDNN_ATTR_VARIANT_PACK_OVERRIDE_UNIQUE_IDS = 1010, /**< Override tensor UIDs for dynamic shapes. @since cuDNN 9.18.0 */
    CUDNN_ATTR_VARIANT_PACK_OVERRIDE_SHAPES     = 1011, /**< Override shapes for dynamic shapes. @since cuDNN 9.18.0 */
    CUDNN_ATTR_VARIANT_PACK_OVERRIDE_STRIDES    = 1012, /**< Override strides for dynamic shapes. @since cuDNN 9.18.0 */

    /* Layout info attributes */
    CUDNN_ATTR_LAYOUT_INFO_TENSOR_UID = 1100, /**< Associated tensor identifier. @since cuDNN 9.0.0 */
    CUDNN_ATTR_LAYOUT_INFO_TYPES      = 1101, /**< Available memory layout types. @since cuDNN 9.0.0 */

    /* Knob info attributes */
    CUDNN_ATTR_KNOB_INFO_TYPE          = 1200, /**< Knob type being described. @since cuDNN 9.0.0 */
    CUDNN_ATTR_KNOB_INFO_MAXIMUM_VALUE = 1201, /**< Upper bound for knob value. @since cuDNN 9.0.0 */
    CUDNN_ATTR_KNOB_INFO_MINIMUM_VALUE = 1202, /**< Lower bound for knob value. @since cuDNN 9.0.0 */
    CUDNN_ATTR_KNOB_INFO_STRIDE        = 1203, /**< Valid increment between knob values. @since cuDNN 9.0.0 */

    /* Engine attributes */
    CUDNN_ATTR_ENGINE_OPERATION_GRAPH             = 1300, /**< Operation graph this engine processes. @since cuDNN 9.0.0 */
    CUDNN_ATTR_ENGINE_GLOBAL_INDEX                = 1301, /**< Engine index in the global engine list. @since cuDNN 9.0.0 */
    CUDNN_ATTR_ENGINE_KNOB_INFO                   = 1302, /**< Available knob configuration options. @since cuDNN 9.0.0 */
    CUDNN_ATTR_ENGINE_NUMERICAL_NOTE              = 1303, /**< Numerical properties (tensor cores, precision). @since cuDNN 9.0.0 */
    CUDNN_ATTR_ENGINE_LAYOUT_INFO                 = 1304, /**< Preferred tensor memory layouts. @since cuDNN 9.0.0 */
    CUDNN_ATTR_ENGINE_BEHAVIOR_NOTE               = 1305, /**< Runtime behavior characteristics. @since cuDNN 9.0.0 */
    CUDNN_ATTR_ENGINE_SM_COUNT_TARGET             = 1306, /**< Streaming multiprocessor target count. @since cuDNN 9.0.0 */
    CUDNN_ATTR_ENGINE_DEVICEPROP                  = 1307, /**< Device properties descriptor. @since cuDNN 9.8.0 */
    CUDNN_ATTR_ENGINE_DISABLE_CLUSTER_COOPERATIVE = 1308, /**< Disable cluster cooperative kernels. @since cuDNN 9.17.0 */

    /* Matmul attributes */
    CUDNN_ATTR_MATMUL_COMP_TYPE     = 1500, /**< Computation precision type for matmul. @since cuDNN 9.0.0 */
    CUDNN_ATTR_MATMUL_PADDING_VALUE = 1503, /**< Padding value for incomplete blocks. @since cuDNN 9.0.0 */

    /* Operation matmul attributes */
    CUDNN_ATTR_OPERATION_MATMUL_ADESC                                                 = 1520, /**< First input matrix (A) tensor descriptor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_MATMUL_BDESC                                                 = 1521, /**< Second input matrix (B) tensor descriptor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_MATMUL_CDESC                                                 = 1522, /**< Output matrix (C) tensor descriptor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_MATMUL_DESC                                                  = 1523, /**< MatMul operation configuration descriptor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_MATMUL_IRREGULARLY_STRIDED_BATCH_COUNT CUDNN_DEPRECATED_ENUM = 1524, /**< Irregular batch count. @deprecated @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_MATMUL_GEMM_M_OVERRIDE_DESC                                  = 1525, /**< Override for output rows (M). @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_MATMUL_GEMM_N_OVERRIDE_DESC                                  = 1526, /**< Override for output columns (N). @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_MATMUL_GEMM_K_OVERRIDE_DESC                                  = 1527, /**< Override for contraction dim (K). @since cuDNN 9.0.0 */

    /* Reduction attributes */
    CUDNN_ATTR_REDUCTION_OPERATOR         = 1600, /**< Reduction operation type (ADD, MUL, MIN, etc.). @since cuDNN 9.0.0 */
    CUDNN_ATTR_REDUCTION_COMP_TYPE        = 1601, /**< Computation data type for reduction. @since cuDNN 9.0.0 */
    CUDNN_ATTR_REDUCTION_IS_DETERMINISTIC = 1602, /**< Whether reduction must be deterministic. @since cuDNN 9.11.0 */

    /* Operation reduction attributes */
    CUDNN_ATTR_OPERATION_REDUCTION_XDESC = 1610, /**< Reduction input tensor descriptor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_REDUCTION_YDESC = 1611, /**< Reduction output tensor descriptor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_REDUCTION_DESC  = 1612, /**< Reduction descriptor reference. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_REDUCTION_GROUP_OFFSET_DESC =
        1613, /**< Optional group offset tensor descriptor for grouped reduction. @since cuDNN 9.24.0 */

    /* Operation BN backward weights attributes */
    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_MATH_PREC        = 1620, /**< Computation precision. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_MEAN_DESC        = 1621, /**< Cached batch mean from forward. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_INVSTD_DESC      = 1622, /**< Cached inverse std dev from forward. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_BN_SCALE_DESC    = 1623, /**< Batch norm scale parameter. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_X_DESC           = 1624, /**< Forward input tensor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_DY_DESC          = 1625, /**< Output gradient tensor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_DBN_SCALE_DESC   = 1626, /**< Scale gradient output. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_DBN_BIAS_DESC    = 1627, /**< Bias gradient output. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_EQ_DY_SCALE_DESC = 1628, /**< Equivalent output gradient scale. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_EQ_X_SCALE_DESC  = 1629, /**< Equivalent input scale. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_EQ_BIAS          = 1630, /**< Equivalent bias value. @since cuDNN 9.0.0 */

    /* Resample attributes */
    CUDNN_ATTR_RESAMPLE_MODE            = 1700, /**< Resampling interpolation method. @since cuDNN 9.0.0 */
    CUDNN_ATTR_RESAMPLE_COMP_TYPE       = 1701, /**< Computation precision for resampling. @since cuDNN 9.0.0 */
    CUDNN_ATTR_RESAMPLE_SPATIAL_DIMS    = 1702, /**< Number of spatial dimensions. @since cuDNN 9.0.0 */
    CUDNN_ATTR_RESAMPLE_POST_PADDINGS   = 1703, /**< Post-paddings per spatial dimension. @since cuDNN 9.0.0 */
    CUDNN_ATTR_RESAMPLE_PRE_PADDINGS    = 1704, /**< Pre-paddings per spatial dimension. @since cuDNN 9.0.0 */
    CUDNN_ATTR_RESAMPLE_STRIDES         = 1705, /**< Stride factors per spatial dimension. @since cuDNN 9.0.0 */
    CUDNN_ATTR_RESAMPLE_WINDOW_DIMS     = 1706, /**< Filter window sizes per dimension. @since cuDNN 9.0.0 */
    CUDNN_ATTR_RESAMPLE_NAN_PROPAGATION = 1707, /**< NaN handling behavior. @since cuDNN 9.0.0 */
    CUDNN_ATTR_RESAMPLE_PADDING_MODE    = 1708, /**< Padding strategy (zero, neg_inf, edge). @since cuDNN 9.0.0 */

    /* Operation resample forward attributes */
    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_XDESC                       = 1710, /**< Forward resample input tensor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_YDESC                       = 1711, /**< Forward resample output tensor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_IDXDESC                     = 1712, /**< Max pooling index tensor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_ALPHA CUDNN_DEPRECATED_ENUM = 1713, /**< Output scaling factor. @deprecated @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_BETA CUDNN_DEPRECATED_ENUM  = 1714, /**< Accumulation scaling factor. @deprecated @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_DESC                        = 1716, /**< Resample descriptor reference. @since cuDNN 9.0.0 */

    /* Operation resample backward attributes */
    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_DXDESC                      = 1720, /**< Backward resample input gradient. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_DYDESC                      = 1721, /**< Backward resample output gradient. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_IDXDESC                     = 1722, /**< Index tensor from forward pass. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_ALPHA CUDNN_DEPRECATED_ENUM = 1723, /**< Gradient scaling factor. @deprecated @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_BETA CUDNN_DEPRECATED_ENUM  = 1724, /**< Accumulation scaling. @deprecated @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_DESC                        = 1725, /**< Resample descriptor reference. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_XDESC                       = 1726, /**< Forward input reference. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_YDESC                       = 1727, /**< Forward output reference. @since cuDNN 9.0.0 */

    /* Operation concat attributes */
    CUDNN_ATTR_OPERATION_CONCAT_AXIS          = 1800, /**< Concatenation axis dimension. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_CONCAT_INPUT_DESCS   = 1801, /**< Array of input tensor descriptors. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_CONCAT_INPLACE_INDEX = 1802, /**< In-place output tensor selection index. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_CONCAT_OUTPUT_DESC   = 1803, /**< Concatenated output descriptor. @since cuDNN 9.0.0 */

    /* Operation signal attributes */
    CUDNN_ATTR_OPERATION_SIGNAL_MODE     = 1900, /**< Signal set vs wait mode. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_SIGNAL_FLAGDESC = 1901, /**< Flag variable tensor descriptor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_SIGNAL_VALUE    = 1902, /**< Signal value for comparison. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_SIGNAL_XDESC    = 1903, /**< Input tensor for signal set. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_SIGNAL_YDESC    = 1904, /**< Output tensor descriptor. @since cuDNN 9.0.0 */

    /* Operation paged cache load attributes */
    CUDNN_ATTR_OPERATION_PAGED_CACHE_LOAD_CONTAINER_DESC  = 1950, /**< Cache container descriptor. @since cuDNN 9.4.0 */
    CUDNN_ATTR_OPERATION_PAGED_CACHE_LOAD_YDESC           = 1951, /**< Output tensor descriptor. @since cuDNN 9.4.0 */
    CUDNN_ATTR_OPERATION_PAGED_CACHE_LOAD_SEQUENCE_DESC   = 1952, /**< Load sequence specification. @since cuDNN 9.4.0 */
    CUDNN_ATTR_OPERATION_PAGED_CACHE_LOAD_PAGE_TABLE_DESC = 1953, /**< Page table mapping descriptor. @since cuDNN 9.4.0 */

    /* Operation norm forward attributes */
    CUDNN_ATTR_OPERATION_NORM_FWD_MODE                     = 2000, /**< Normalization algorithm type. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_NORM_FWD_PHASE                    = 2001, /**< Training vs inference phase. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_NORM_FWD_XDESC                    = 2002, /**< Input tensor descriptor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_NORM_FWD_MEAN_DESC                = 2003, /**< Computed or cached mean tensor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_NORM_FWD_INV_VARIANCE_DESC        = 2004, /**< Computed or cached inverse variance. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_NORM_FWD_SCALE_DESC               = 2005, /**< Learnable scale parameter (gamma). @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_NORM_FWD_BIAS_DESC                = 2006, /**< Learnable bias parameter (beta). @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_NORM_FWD_EPSILON_DESC             = 2007, /**< Numerical stability constant. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_NORM_FWD_EXP_AVG_FACTOR_DESC      = 2008, /**< Momentum for running statistics. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_NORM_FWD_INPUT_RUNNING_MEAN_DESC  = 2009, /**< Input running mean. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_NORM_FWD_INPUT_RUNNING_VAR_DESC   = 2010, /**< Input running variance. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_NORM_FWD_OUTPUT_RUNNING_MEAN_DESC = 2011, /**< Updated running mean output. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_NORM_FWD_OUTPUT_RUNNING_VAR_DESC  = 2012, /**< Updated running variance output. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_NORM_FWD_YDESC                    = 2013, /**< Output tensor descriptor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_NORM_FWD_PEER_STAT_DESCS          = 2014, /**< Peer statistics for multi-GPU sync. @since cuDNN 9.0.0 */

    /* Operation norm backward attributes */
    CUDNN_ATTR_OPERATION_NORM_BWD_MODE              = 2100, /**< Normalization algorithm type. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_NORM_BWD_XDESC             = 2101, /**< Forward input tensor reference. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_NORM_BWD_MEAN_DESC         = 2102, /**< Cached mean from forward pass. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_NORM_BWD_INV_VARIANCE_DESC = 2103, /**< Cached inverse std dev. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_NORM_BWD_DYDESC            = 2104, /**< Output gradient tensor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_NORM_BWD_SCALE_DESC        = 2105, /**< Forward scale parameter. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_NORM_BWD_EPSILON_DESC      = 2106, /**< Numerical stability epsilon. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_NORM_BWD_DSCALE_DESC       = 2107, /**< Scale gradient output. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_NORM_BWD_DBIAS_DESC        = 2108, /**< Bias gradient output. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_NORM_BWD_DXDESC            = 2109, /**< Input gradient output tensor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_NORM_BWD_PEER_STAT_DESCS   = 2110, /**< Peer gradient statistics for multi-GPU. @since cuDNN 9.0.0 */

    /* Operation reshape attributes */
    CUDNN_ATTR_OPERATION_RESHAPE_XDESC = 2200, /**< Reshape input tensor descriptor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_RESHAPE_YDESC = 2201, /**< Reshape output tensor descriptor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_RESHAPE_MODE  = 2202, /**< Reshape mode (view-only or logical). @since cuDNN 9.22.0 */

    /* Operation transpose attributes */
    CUDNN_ATTR_OPERATION_TRANSPOSE_XDESC       = 3200, /**< Transpose input tensor descriptor. @since cuDNN 9.22.0 */
    CUDNN_ATTR_OPERATION_TRANSPOSE_YDESC       = 3201, /**< Transpose output tensor descriptor. @since cuDNN 9.22.0 */
    CUDNN_ATTR_OPERATION_TRANSPOSE_PERMUTATION = 3202, /**< Transpose permutation array. @since cuDNN 9.22.0 */

    /* Operation slice attributes */
    CUDNN_ATTR_OPERATION_SLICE_XDESC         = 3300, /**< Slice input tensor descriptor. @since cuDNN 9.22.0 */
    CUDNN_ATTR_OPERATION_SLICE_YDESC         = 3301, /**< Slice output tensor descriptor. @since cuDNN 9.22.0 */
    CUDNN_ATTR_OPERATION_SLICE_START_INDICES = 3302, /**< Slice start indices. @since cuDNN 9.22.0 */
    CUDNN_ATTR_OPERATION_SLICE_LIMIT_INDICES = 3303, /**< Slice limit indices. @since cuDNN 9.22.0 */
    CUDNN_ATTR_OPERATION_SLICE_STRIDES       = 3304, /**< Slice strides. @since cuDNN 9.22.0 */

    /* Operation RoPE forward attributes */
    CUDNN_ATTR_OPERATION_ROPE_FWD_XDESC     = 3400, /**< RoPE input tensor descriptor [B,H,S,D]. @since cuDNN 9.24.0 */
    CUDNN_ATTR_OPERATION_ROPE_FWD_YDESC     = 3401, /**< RoPE output tensor descriptor [B,H,S,D]. @since cuDNN 9.24.0 */
    CUDNN_ATTR_OPERATION_ROPE_FWD_FREQSDESC = 3402, /**< RoPE frequency angles tensor [S,1,1,D]. Kernel computes sincosf internally. @since cuDNN 9.24.0 */
    CUDNN_ATTR_OPERATION_ROPE_FWD_OUTPUT_SCALE = 3403, /**< RoPE host-side fp32 scalar multiplied into cos/sin (default 1.0). Used to fold YARN mscale and/or SDPA attn_scale. @since cuDNN 9.24.0 */
    CUDNN_ATTR_OPERATION_ROPE_FWD_ROPE_DIM     = 3404, /**< RoPE rotation width in last dim (int64, default = head_dim). When < head_dim, the last rope_dim slots get rotated; the first (head_dim - rope_dim) are scaled-pass-through (used for DSv3-style MLA). @since cuDNN 9.24.0 */

    /* Operation RoPE backward attributes */
    CUDNN_ATTR_OPERATION_ROPE_BWD_DYDESC        = 3410, /**< RoPE bwd input gradient (dY) descriptor [B,H,S,D]. @since cuDNN 9.25.0 */
    CUDNN_ATTR_OPERATION_ROPE_BWD_DXDESC        = 3411, /**< RoPE bwd output gradient (dX) descriptor [B,H,S,D]. @since cuDNN 9.25.0 */
    CUDNN_ATTR_OPERATION_ROPE_BWD_FREQSDESC     = 3412, /**< RoPE bwd frequency angles tensor [S,1,1,D]; same semantics as fwd. @since cuDNN 9.25.0 */
    CUDNN_ATTR_OPERATION_ROPE_BWD_OUTPUT_SCALE  = 3413, /**< RoPE bwd host fp32 scalar (default 1.0). Symmetric to fwd: cos/sin pre-scaled, passthrough scaled. @since cuDNN 9.25.0 */
    CUDNN_ATTR_OPERATION_ROPE_BWD_ROPE_DIM      = 3414, /**< RoPE bwd rotation width in last dim (int64, default = head_dim). @since cuDNN 9.25.0 */

    /* Operation expand band matrix attributes */
    CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_XDESC                 = 2250, /**< Band matrix input tensor. @since cuDNN 9.10.0 */
    CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_YDESC                 = 2251, /**< Expanded output tensor. @since cuDNN 9.10.0 */
    CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_LOWER_BANDWIDTH       = 2252, /**< Lower bandwidth of the band. @since cuDNN 9.10.0 */
    CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_UPPER_BANDWIDTH       = 2253, /**< Upper bandwidth of the band. @since cuDNN 9.10.0 */
    CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_AXIS                  = 2254, /**< Axis along which to expand. @since cuDNN 9.10.0 */
    CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_PAD_VALUE             = 2255, /**< Padding value outside the band. @since cuDNN 9.10.0 */
    CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_KV_TOKEN_OFFSET_DESC  = 2256, /**< KV token offset descriptor. @since cuDNN 9.10.0 */
    CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_SPECULATIVE_MASK_DESC = 2257, /**< Speculative decoding mask. @since cuDNN 9.13.0 */

    /* Operation contract band matrix attributes */
    CUDNN_ATTR_OPERATION_CONTRACT_BAND_MATRIX_XDESC           = 2270, /**< Full matrix input tensor. @since cuDNN 9.10.0 */
    CUDNN_ATTR_OPERATION_CONTRACT_BAND_MATRIX_YDESC           = 2271, /**< Contracted band output tensor. @since cuDNN 9.10.0 */
    CUDNN_ATTR_OPERATION_CONTRACT_BAND_MATRIX_LOWER_BANDWIDTH = 2272, /**< Lower bandwidth. @since cuDNN 9.10.0 */
    CUDNN_ATTR_OPERATION_CONTRACT_BAND_MATRIX_UPPER_BANDWIDTH = 2273, /**< Upper bandwidth. @since cuDNN 9.10.0 */
    CUDNN_ATTR_OPERATION_CONTRACT_BAND_MATRIX_AXIS            = 2274, /**< Axis along which to contract. @since cuDNN 9.10.0 */
    CUDNN_ATTR_OPERATION_CONTRACT_BAND_MATRIX_PAD_VALUE       = 2275, /**< Padding value. @since cuDNN 9.10.0 */
    CUDNN_ATTR_OPERATION_CONTRACT_BAND_MAX_TOKEN_VALUE        = 2276, /**< Maximum token value for contraction. @since cuDNN 9.10.0 */

    /* RNG attributes */
    CUDNN_ATTR_RNG_DISTRIBUTION                   = 2300, /**< Random distribution type selection. @since cuDNN 9.0.0 */
    CUDNN_ATTR_RNG_NORMAL_DIST_MEAN               = 2301, /**< Normal distribution mean parameter. @since cuDNN 9.0.0 */
    CUDNN_ATTR_RNG_NORMAL_DIST_STANDARD_DEVIATION = 2302, /**< Normal distribution std deviation. @since cuDNN 9.0.0 */
    CUDNN_ATTR_RNG_UNIFORM_DIST_MAXIMUM           = 2303, /**< Uniform distribution upper bound. @since cuDNN 9.0.0 */
    CUDNN_ATTR_RNG_UNIFORM_DIST_MINIMUM           = 2304, /**< Uniform distribution lower bound. @since cuDNN 9.0.0 */
    CUDNN_ATTR_RNG_BERNOULLI_DIST_PROBABILITY     = 2305, /**< Bernoulli probability of 1. @since cuDNN 9.0.0 */

    /* Operation RNG attributes */
    CUDNN_ATTR_OPERATION_RNG_YDESC       = 2310, /**< RNG output tensor descriptor. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_RNG_SEED        = 2311, /**< RNG seed value. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_RNG_DESC        = 2312, /**< RNG descriptor reference. @since cuDNN 9.0.0 */
    CUDNN_ATTR_OPERATION_RNG_OFFSET_DESC = 2313, /**< RNG offset/state descriptor. @since cuDNN 9.0.0 */

    /* Kernel cache attributes */
    CUDNN_ATTR_KERNEL_CACHE_OPERATION_GRAPH            = 2400, /**< Operation graph for kernel cache. @since cuDNN 9.5.0 */
    CUDNN_ATTR_KERNEL_CACHE_IS_ENGINECFG_KERNEL_CACHED = 2401, /**< Whether kernel is cached. @since cuDNN 9.4.0 */
    CUDNN_ATTR_KERNEL_CACHE_JSON_REPRESENTATION        = 2402, /**< Kernel cache JSON serialization. @since cuDNN 9.10.0 */

    /* Operation block-scale quantize attributes */
    CUDNN_ATTR_OPERATION_BLOCK_SCALE_QUANTIZE_XDESC      = 2500, /**< Input float tensor to quantize. @since cuDNN 9.7.0 */
    CUDNN_ATTR_OPERATION_BLOCK_SCALE_QUANTIZE_YDESC      = 2501, /**< Quantized output tensor. @since cuDNN 9.7.0 */
    CUDNN_ATTR_OPERATION_BLOCK_SCALE_QUANTIZE_SCALE_DESC = 2502, /**< Per-block scaling factors output. @since cuDNN 9.7.0 */
    CUDNN_ATTR_OPERATION_BLOCK_SCALE_QUANTIZE_MATH_PREC  = 2503, /**< Computation precision. @since cuDNN 9.7.0 */
    CUDNN_ATTR_OPERATION_BLOCK_SCALE_QUANTIZE_BLOCK_SIZE = 2504, /**< Quantization block size. @since cuDNN 9.7.0 */

    /* Operation block-scale dequantize attributes */
    CUDNN_ATTR_OPERATION_BLOCK_SCALE_DEQUANTIZE_XDESC      = 2600, /**< Quantized input tensor. @since cuDNN 9.7.0 */
    CUDNN_ATTR_OPERATION_BLOCK_SCALE_DEQUANTIZE_SCALE_DESC = 2601, /**< Per-block scale factors. @since cuDNN 9.7.0 */
    CUDNN_ATTR_OPERATION_BLOCK_SCALE_DEQUANTIZE_YDESC      = 2602, /**< Dequantized output tensor. @since cuDNN 9.7.0 */
    CUDNN_ATTR_OPERATION_BLOCK_SCALE_DEQUANTIZE_MATH_PREC  = 2603, /**< Computation precision. @since cuDNN 9.7.0 */
    CUDNN_ATTR_OPERATION_BLOCK_SCALE_DEQUANTIZE_BLOCK_SIZE = 2604, /**< Dequantization block size. @since cuDNN 9.7.0 */
    CUDNN_ATTR_OPERATION_BLOCK_SCALE_DEQUANTIZE_NEG_SCALE  = 2605, /**< Negative scale handling. @since cuDNN 9.13.0 */

    /* Device property attributes */
    CUDNN_ATTR_DEVICEPROP_DEVICE_ID           = 2700, /**< CUDA device identifier. @since cuDNN 9.8.0 */
    CUDNN_ATTR_DEVICEPROP_HANDLE              = 2701, /**< Associated cuDNN handle. @since cuDNN 9.8.0 */
    CUDNN_ATTR_DEVICEPROP_JSON_REPRESENTATION = 2702, /**< Device properties JSON. @since cuDNN 9.8.0 */

    /* Operation SDPA forward attributes */
    CUDNN_ATTR_OPERATION_SDPA_FWD_QDESC                 = 2800, /**< Query tensor descriptor. @since cuDNN 9.13.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_KDESC                 = 2801, /**< Key tensor descriptor. @since cuDNN 9.13.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_VDESC                 = 2802, /**< Value tensor descriptor. @since cuDNN 9.13.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_ODESC                 = 2803, /**< Output tensor descriptor. @since cuDNN 9.13.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_STATSDESC             = 2804, /**< Statistics output descriptor. @since cuDNN 9.13.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_SCALEDESC             = 2805, /**< Attention scaling factor descriptor. @since cuDNN 9.13.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_BLOCK_MASK_DESC       = 2806, /**< Block-sparse attention mask. @since cuDNN 9.14.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_PAGE_TABLE_KDESC      = 2807, /**< Paged attention key page table. @since cuDNN 9.15.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_PAGE_TABLE_VDESC      = 2808, /**< Paged attention value page table. @since cuDNN 9.15.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_SEQ_LEN_QDESC         = 2809, /**< Query sequence length tensor. @since cuDNN 9.15.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_SEQ_LEN_KVDESC        = 2810, /**< Key-value sequence length tensor. @since cuDNN 9.15.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_SUBGRAPH              = 2811, /**< Forward SDPA subgraph. @since cuDNN 9.21.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_SUBGRAPH_INPUT_UID    = 2812, /**< Subgraph input tensor UID. @since cuDNN 9.21.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_SUBGRAPH_OUTPUT_UID   = 2813, /**< Subgraph output tensor UID. @since cuDNN 9.21.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_SOFTMAX_DESC          = 2814, /**< Softmax descriptor. @since cuDNN 9.21.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_DROPOUT_SEED_DESC     = 2815, /**< Dropout seed tensor descriptor. @since cuDNN 9.21.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_DROPOUT_OFFSET_DESC   = 2816, /**< Dropout offset tensor descriptor. @since cuDNN 9.21.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_DROPOUT_RNG_DUMP_DESC = 2817, /**< Dropout RNG dump tensor descriptor. @since cuDNN 9.21.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_DROPOUT_PROBABILITY   = 2818, /**< Dropout probability. @since cuDNN 9.21.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_UNFUSE_FMA            = 2819, /**< Unfuse FMA in softmax for SM100. @since cuDNN 9.22.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_CU_SEQ_LEN_QDESC      = 2820, /**< Query cumulative sequence length tensor. @since cuDNN 9.24.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_CU_SEQ_LEN_KVDESC     = 2821, /**< Key-value cumulative sequence length tensor. @since cuDNN 9.24.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_DESCALE_QDESC         = 2822, /**< FP8 descale factor for Q. @since cuDNN 9.25.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_DESCALE_KDESC         = 2823, /**< FP8 descale factor for K. @since cuDNN 9.25.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_DESCALE_VDESC         = 2824, /**< FP8 descale factor for V. @since cuDNN 9.25.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_DESCALE_SDESC         = 2825, /**< FP8 descale factor for S. @since cuDNN 9.25.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_SCALE_SDESC           = 2826, /**< FP8 scale factor for S. @since cuDNN 9.25.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_SCALE_ODESC           = 2827, /**< FP8 scale factor for O. @since cuDNN 9.25.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_AMAX_SDESC            = 2828, /**< FP8 absolute maximum of S. @since cuDNN 9.25.0 */
    CUDNN_ATTR_OPERATION_SDPA_FWD_AMAX_ODESC            = 2829, /**< FP8 absolute maximum of O. @since cuDNN 9.25.0 */

    /* Operation SDPA backward attributes */
    CUDNN_ATTR_OPERATION_SDPA_BWD_QDESC                = 2851, /**< Query tensor descriptor. @since cuDNN 9.17.0 */
    CUDNN_ATTR_OPERATION_SDPA_BWD_KDESC                = 2852, /**< Key tensor descriptor. @since cuDNN 9.17.0 */
    CUDNN_ATTR_OPERATION_SDPA_BWD_VDESC                = 2853, /**< Value tensor descriptor. @since cuDNN 9.17.0 */
    CUDNN_ATTR_OPERATION_SDPA_BWD_ODESC                = 2854, /**< Forward output tensor descriptor. @since cuDNN 9.17.0 */
    CUDNN_ATTR_OPERATION_SDPA_BWD_STATSDESC            = 2855, /**< Forward statistics descriptor. @since cuDNN 9.17.0 */
    CUDNN_ATTR_OPERATION_SDPA_BWD_SCALEDESC            = 2856, /**< Attention scaling factor. @since cuDNN 9.17.0 */
    CUDNN_ATTR_OPERATION_SDPA_BWD_SEQ_LEN_QDESC        = 2857, /**< Query sequence length tensor. @since cuDNN 9.17.0 */
    CUDNN_ATTR_OPERATION_SDPA_BWD_SEQ_LEN_KVDESC       = 2858, /**< Key-value sequence length tensor. @since cuDNN 9.17.0 */
    CUDNN_ATTR_OPERATION_SDPA_BWD_DQDESC               = 2859, /**< Query gradient output tensor. @since cuDNN 9.17.0 */
    CUDNN_ATTR_OPERATION_SDPA_BWD_DKDESC               = 2860, /**< Key gradient output tensor. @since cuDNN 9.17.0 */
    CUDNN_ATTR_OPERATION_SDPA_BWD_DVDESC               = 2861, /**< Value gradient output tensor. @since cuDNN 9.17.0 */
    CUDNN_ATTR_OPERATION_SDPA_BWD_DODDESC              = 2862, /**< Output gradient input tensor. @since cuDNN 9.17.0 */
    CUDNN_ATTR_OPERATION_SDPA_BWD_SINK_DESC            = 2863, /**< Backward sink descriptor. @since UNPUBLISHED */
    CUDNN_ATTR_OPERATION_SDPA_BWD_DSINK_DESC           = 2864, /**< Backward sink gradient descriptor. @since UNPUBLISHED */
    CUDNN_ATTR_OPERATION_SDPA_BWD_MAX_TOTAL_SEQ_LEN_Q  = 2865, /**< Max total query sequence length. @since UNPUBLISHED */
    CUDNN_ATTR_OPERATION_SDPA_BWD_MAX_TOTAL_SEQ_LEN_KV = 2866, /**< Max total KV sequence length. @since UNPUBLISHED */
    CUDNN_ATTR_OPERATION_SDPA_BWD_SUBGRAPH             = 2867, /**< Backward SDPA subgraph. @since UNPUBLISHED */
    CUDNN_ATTR_OPERATION_SDPA_BWD_SUBGRAPH_INPUT_UID   = 2868, /**< Subgraph input tensor UID. @since UNPUBLISHED */
    CUDNN_ATTR_OPERATION_SDPA_BWD_SUBGRAPH_OUTPUT_UID  = 2869, /**< Subgraph output tensor UID. @since UNPUBLISHED */

    /* Operation MoE grouped matmul attributes */
    CUDNN_ATTR_OPERATION_MOE_GROUPED_MATMUL_MODE                    = 2900, /**< Gather/scatter mode for MoE. @since cuDNN 9.15.0 */
    CUDNN_ATTR_OPERATION_MOE_GROUPED_MATMUL_MATH_PREC               = 2901, /**< Computation precision. @since cuDNN 9.15.0 */
    CUDNN_ATTR_OPERATION_MOE_GROUPED_MATMUL_TOKEN_DESC              = 2902, /**< Token tensor descriptor. @since cuDNN 9.15.0 */
    CUDNN_ATTR_OPERATION_MOE_GROUPED_MATMUL_WEIGHT_DESC             = 2903, /**< Expert weight tensor descriptor. @since cuDNN 9.15.0 */
    CUDNN_ATTR_OPERATION_MOE_GROUPED_MATMUL_FIRST_TOKEN_OFFSET_DESC = 2904, /**< First token offset per expert. @since cuDNN 9.15.0 */
    CUDNN_ATTR_OPERATION_MOE_GROUPED_MATMUL_OUTPUT_DESC             = 2905, /**< Output tensor descriptor. @since cuDNN 9.15.0 */
    CUDNN_ATTR_OPERATION_MOE_GROUPED_MATMUL_TOKEN_INDEX_DESC        = 2906, /**< Token routing index descriptor. @since cuDNN 9.15.0 */
    CUDNN_ATTR_OPERATION_MOE_GROUPED_MATMUL_TOKEN_KS_DESC           = 2907, /**< Token routing weights descriptor. @since cuDNN 9.15.0 */
    CUDNN_ATTR_OPERATION_MOE_GROUPED_MATMUL_TOP_K                   = 2908, /**< Top-K experts per token. @since cuDNN 9.15.0 */

    CUDNN_ATTR_OPERATION_MOE_GROUPED_MATMUL_BWD_MATH_PREC               = 2951, /**< Backward math precision for MoE grouped matmul. @since cuDNN 9.22.0 */
    CUDNN_ATTR_OPERATION_MOE_GROUPED_MATMUL_BWD_TOKEN_DESC              = 2952, /**< Backward token descriptor for MoE grouped matmul. @since cuDNN 9.22.0 */
    CUDNN_ATTR_OPERATION_MOE_GROUPED_MATMUL_BWD_DWEIGHT_DESC            = 2953, /**< Backward weight descriptor for MoE grouped matmul. @since cuDNN 9.22.0 */
    CUDNN_ATTR_OPERATION_MOE_GROUPED_MATMUL_BWD_FIRST_TOKEN_OFFSET_DESC = 2954, /**< Backward first token offset descriptor for MoE grouped matmul. @since cuDNN 9.22.0 */
    CUDNN_ATTR_OPERATION_MOE_GROUPED_MATMUL_BWD_DOUTPUT_DESC            = 2955, /**< Backward output descriptor for MoE grouped matmul. @since cuDNN 9.22.0 */

    /* Operation diagonal band mask attributes */
    CUDNN_ATTR_OPERATION_DIAGONAL_BAND_MASK_XDESC                  = 3000, /**< Input tensor descriptor. @since cuDNN 9.20.0 */
    CUDNN_ATTR_OPERATION_DIAGONAL_BAND_MASK_SEQ_LEN_KVDESC         = 3001, /**< KV sequence length tensor. @since cuDNN 9.20.0 */
    CUDNN_ATTR_OPERATION_DIAGONAL_BAND_MASK_SEQ_LEN_QDESC          = 3002, /**< Query sequence length tensor. @since cuDNN 9.20.0 */
    CUDNN_ATTR_OPERATION_DIAGONAL_BAND_MASK_LEFT_BOUND_DESC        = 3003, /**< Left bound offset descriptor. @since cuDNN 9.20.0 */
    CUDNN_ATTR_OPERATION_DIAGONAL_BAND_MASK_SHIFT_RIGHT_BOUND_DESC = 3004, /**< Right bound shift descriptor. @since cuDNN 9.20.0 */
    CUDNN_ATTR_OPERATION_DIAGONAL_BAND_MASK_BDESC                  = 3005, /**< Band descriptor. @since cuDNN 9.20.0 */
    CUDNN_ATTR_OPERATION_DIAGONAL_BAND_MASK_YDESC                  = 3006, /**< Output mask tensor descriptor. @since cuDNN 9.20.0 */
    CUDNN_ATTR_OPERATION_DIAGONAL_BAND_MASK_COMPARISON_MODE        = 3007, /**< Mask comparison mode. @since cuDNN 9.20.0 */
    CUDNN_ATTR_OPERATION_DIAGONAL_BAND_MASK_CU_SEQ_LEN_KVDESC      = 3008, /**< Key-value cumulative sequence length tensor. @since cuDNN 9.24.0 */
    CUDNN_ATTR_OPERATION_DIAGONAL_BAND_MASK_CU_SEQ_LEN_QDESC       = 3009, /**< Query cumulative sequence length tensor. @since cuDNN 9.24.0 */

    /* Operation softmax attributes */
    CUDNN_ATTR_OPERATION_SOFTMAX_XDESC        = 3100, /**< Softmax input tensor descriptor. @since cuDNN 9.20.0 */
    CUDNN_ATTR_OPERATION_SOFTMAX_YDESC        = 3101, /**< Softmax output tensor descriptor. @since cuDNN 9.20.0 */
    CUDNN_ATTR_OPERATION_SOFTMAX_STATS_DESC   = 3102, /**< Softmax statistics output. @since cuDNN 9.20.0 */
    CUDNN_ATTR_OPERATION_SOFTMAX_MAX_DESC     = 3103, /**< Row-wise max values descriptor. @since cuDNN 9.20.0 */
    CUDNN_ATTR_OPERATION_SOFTMAX_SUM_EXP_DESC = 3104, /**< Row-wise sum of exponentials. @since cuDNN 9.20.0 */
    CUDNN_ATTR_OPERATION_SOFTMAX_SINK_DESC    = 3105, /**< Softmax sink descriptor. @since cuDNN 9.20.0 */
} cudnnBackendAttributeName_t;

/** @brief Attribute data types used by the cuDNN backend API for get/set operations.
 *  @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_TYPE_HANDLE                                = 0, /**< cudnnHandle_t value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_DATA_TYPE                             = 1, /**< cudnnDataType_t value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_BOOLEAN                               = 2, /**< Boolean value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_INT64                                 = 3, /**< 64-bit signed integer value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_FLOAT                                 = 4, /**< 32-bit float value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_DOUBLE                                = 5, /**< 64-bit double value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_VOID_PTR                              = 6, /**< Void pointer value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_CONVOLUTION_MODE                      = 7, /**< cudnnConvolutionMode_t value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_HEUR_MODE                             = 8, /**< cudnnBackendHeurMode_t value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_KNOB_TYPE                             = 9, /**< cudnnBackendKnobType_t value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_NAN_PROPOGATION CUDNN_DEPRECATED_ENUM = 10, /**< cudnnNanPropagation_t value. @deprecated @since cuDNN 9.0.0 */
    CUDNN_TYPE_NUMERICAL_NOTE                        = 11, /**< cudnnBackendNumericalNote_t value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_LAYOUT_TYPE                           = 12, /**< cudnnBackendLayoutType_t value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_ATTRIB_NAME                           = 13, /**< cudnnBackendAttributeName_t value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_POINTWISE_MODE                        = 14, /**< cudnnPointwiseMode_t value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_BACKEND_DESCRIPTOR                    = 15, /**< cudnnBackendDescriptor_t value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_GENSTATS_MODE                         = 16, /**< cudnnGenStatsMode_t value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_BN_FINALIZE_STATS_MODE                = 17, /**< cudnnBnFinalizeStatsMode_t value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_REDUCTION_OPERATOR_TYPE               = 18, /**< cudnnReduceTensorOp_t value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_BEHAVIOR_NOTE                         = 19, /**< cudnnBackendBehaviorNote_t value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_TENSOR_REORDERING_MODE                = 20, /**< cudnnBackendTensorReordering_t value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_RESAMPLE_MODE                         = 21, /**< cudnnResampleMode_t value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_PADDING_MODE                          = 22, /**< cudnnPaddingMode_t value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_INT32                                 = 23, /**< 32-bit signed integer value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_CHAR                                  = 24, /**< Character value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_SIGNAL_MODE                           = 25, /**< cudnnSignalMode_t value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_FRACTION                              = 26, /**< cudnnFraction_t value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_NORM_MODE                             = 27, /**< cudnnBackendNormMode_t value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_NORM_FWD_PHASE                        = 28, /**< cudnnBackendNormFwdPhase_t value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_RNG_DISTRIBUTION                      = 29, /**< cudnnRngDistribution_t value. @since cuDNN 9.0.0 */
    CUDNN_TYPE_MOE_GROUPED_MATMUL_MODE               = 30, /**< cudnnMoeGroupedMatmulMode_t value. @since cuDNN 9.15.0 */
    CUDNN_TYPE_RESHAPE_MODE                          = 31, /**< cudnnBackendReshapeMode_t value. @since cuDNN 9.22.0 */
    CUDNN_TYPE_OPERATIONGRAPH_MODE                            = 32, /**< cudnnBackendOperationGraphMode_t value. @since cuDNN 9.NEXT */
} cudnnBackendAttributeType_t;

/** @brief Backend descriptor types identifying the kind of descriptor to create.
 *  @since cuDNN 9.0.0
 */
typedef enum {
    /**
     * @brief Specifies parameters for a pointwise operator.
     *
     *
     * Created with cudnnBackendCreateDescriptor(CUDNN_BACKEND_POINTWISE_DESCRIPTOR, &desc);
     * the cuDNN backend pointwise descriptor specifies the parameters for a
     * pointwise operator like mode, math precision, nan propagation, and so on.
     *
     * Supported attributes (prefix CUDNN_ATTR_POINTWISE_):
     *
     * - CUDNN_ATTR_POINTWISE_MODE (CUDNN_TYPE_POINTWISE_MODE, 1 element)
     *       Mode of the pointwise operation. Required attribute.
     *
     * - CUDNN_ATTR_POINTWISE_MATH_PREC (CUDNN_TYPE_DATA_TYPE, 1 element)
     *       The math precision of the computation. Required attribute.
     *
     * - CUDNN_ATTR_POINTWISE_NAN_PROPAGATION (CUDNN_TYPE_NAN_PROPOGATION, 1 element)
     *       Specifies a method by which to propagate NaNs. Required only for
     *       comparison based pointwise modes, like ReLU. Current support only
     *       includes enum value CUDNN_PROPAGATE_NAN. Default value is
     *       CUDNN_NOT_PROPAGATE_NAN.
     *
     * - CUDNN_ATTR_POINTWISE_RELU_LOWER_CLIP (CUDNN_TYPE_DOUBLE/CUDNN_TYPE_FLOAT, 1 element)
     *       Sets the lower clip value for ReLU. If (value < lower_clip)
     *       value = lower_clip + lower_clip_slope * (value - lower_clip).
     *       Default value is 0.0f.
     *
     * - CUDNN_ATTR_POINTWISE_RELU_UPPER_CLIP (CUDNN_TYPE_DOUBLE/CUDNN_TYPE_FLOAT, 1 element)
     *       Sets the upper clip value for ReLU. If (value > upper_clip)
     *       value = upper_clip. Default value is Numeric limit max.
     *
     * - CUDNN_ATTR_POINTWISE_RELU_LOWER_CLIP_SLOPE (CUDNN_TYPE_DOUBLE/CUDNN_TYPE_FLOAT, 1 element)
     *       Sets the lower clip slope value for ReLU. If (value < lower_clip)
     *       value = lower_clip + lower_clip_slope * (value - lower_clip).
     *       Default value is 0.0f.
     *
     * - CUDNN_ATTR_POINTWISE_ELU_ALPHA (CUDNN_TYPE_DOUBLE/CUDNN_TYPE_FLOAT, 1 element)
     *       Sets the alpha value for ELU. If (value < 0.0)
     *       value = alpha * (e^value - 1.0). Default value is 1.0f.
     *
     * - CUDNN_ATTR_POINTWISE_SOFTPLUS_BETA (CUDNN_TYPE_DOUBLE/CUDNN_TYPE_FLOAT, 1 element)
     *       Sets the beta value for softplus. If value = log(1 + e^(beta * value)) / beta.
     *       Default value is 1.0f.
     *
     * - CUDNN_ATTR_POINTWISE_SWISH_BETA (CUDNN_TYPE_DOUBLE/CUDNN_TYPE_FLOAT, 1 element)
     *       Sets the beta value for swish. If value = value / (1 + e^(-beta * value)).
     *       Default value is 1.0f.
     *
     * - CUDNN_ATTR_POINTWISE_AXIS (CUDNN_TYPE_INT64, 1 element)
     *       Sets the axis value for GEN_INDEX. The index will be generated for
     *       this axis. Default value is -1. Needs to lie between
     *       [0, input_dim_size-1]. For example, if your input has dimensions
     *       [N,C,H,W], the axis can be set to anything in [0,3].
     *
     * Finalization:
     *   CUDNN_STATUS_SUCCESS - The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_POINTWISE_DESCRIPTOR                             = 0, /**< Pointwise op config: mode, math precision, activation params. @since cuDNN 9.0.0 */
    /**
     * @brief Specifies parameters for a convolution operator.
     *
     *
     * Created with cudnnBackendCreateDescriptor(CUDNN_BACKEND_CONVOLUTION_DESCRIPTOR, &desc);
     * the cuDNN backend convolution descriptor specifies the parameters for a
     * convolution operator for both forward and backward propagation: compute
     * data type, convolution mode, filter dilation and stride, and padding on
     * both sides.
     *
     * Supported attributes (prefix CUDNN_ATTR_CONVOLUTION_):
     *
     * - CUDNN_ATTR_CONVOLUTION_COMP_TYPE (CUDNN_TYPE_DATA_TYPE, 1 element)
     *       The compute type of the convolution operator. Required attribute.
     *
     * - CUDNN_ATTR_CONVOLUTION_MODE (CUDNN_TYPE_CONVOLUTION_MODE, 1 element)
     *       Convolution or cross-correlation mode. Required attribute.
     *
     * - CUDNN_ATTR_CONVOLUTION_SPATIAL_DIMS (CUDNN_TYPE_INT64, 1 element)
     *       The number of spatial dimensions, expected array length for each of
     *       dilations, filter strides, and padding arrays. Required attribute.
     *
     * - CUDNN_ATTR_CONVOLUTION_DILATIONS (CUDNN_TYPE_INT64, 1..CUDNN_MAX_DIMS elements)
     *       Filter dilation. Required attribute.
     *
     * - CUDNN_ATTR_CONVOLUTION_FILTER_STRIDES (CUDNN_TYPE_INT64, 1..CUDNN_MAX_DIMS elements)
     *       Filter stride. Required attribute.
     *
     * - CUDNN_ATTR_CONVOLUTION_PRE_PADDINGS (CUDNN_TYPE_INT64, 1..CUDNN_MAX_DIMS elements)
     *       Padding at the beginning of each spatial dimension. Required attribute.
     *
     * - CUDNN_ATTR_CONVOLUTION_POST_PADDINGS (CUDNN_TYPE_INT64, 1..CUDNN_MAX_DIMS elements)
     *       Padding at the end of each spatial dimension. Required attribute.
     *
     * Finalization:
     *   CUDNN_STATUS_BAD_PARAM - An elemCount argument for setting
     *       CUDNN_ATTR_CONVOLUTION_DILATIONS, CUDNN_ATTR_CONVOLUTION_FILTER_STRIDES,
     *       CUDNN_ATTR_CONVOLUTION_PRE_PADDINGS, and CUDNN_ATTR_CONVOLUTION_POST_PADDINGS
     *       is not equal to the value set for CUDNN_ATTR_CONVOLUTION_SPATIAL_DIMS.
     *   CUDNN_STATUS_SUCCESS - The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_CONVOLUTION_DESCRIPTOR                           = 1, /**< Convolution config: compute type, mode, dilation, stride, padding. @since cuDNN 9.0.0 */
    /**
     * @brief Describes an engine to compute an operation graph.
     *
     *
     * Created with descriptor type value CUDNN_BACKEND_ENGINE_DESCRIPTOR, cuDNN
     * backend engine descriptor describes an engine to compute an operation
     * graph. An engine is a grouping of kernels with similar compute and
     * numerical attributes.
     *
     * Supported attributes (prefix CUDNN_ATTR_ENGINE_):
     *
     * - CUDNN_ATTR_ENGINE_OPERATION_GRAPH (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element)
     *       The operation graph to compute. Descriptor type
     *       CUDNN_BACKEND_OPERATIONGRAPH_DESCRIPTOR. Required attribute.
     *
     * - CUDNN_ATTR_ENGINE_GLOBAL_INDEX (CUDNN_TYPE_INT64, 1 element)
     *       The index for the engine. Valid values are between 0 and
     *       CUDNN_ATTR_OPERATIONGRAPH_ENGINE_GLOBAL_COUNT-1. Required attribute.
     *
     * - CUDNN_ATTR_ENGINE_KNOB_INFO (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element)
     *       The descriptors of performance knobs of the engine. Descriptor type
     *       CUDNN_BACKEND_KNOB_INFO_DESCRIPTOR. Read-only attribute.
     *
     * - CUDNN_ATTR_ENGINE_NUMERICAL_NOTE (CUDNN_TYPE_NUMERICAL_NOTE, 0+ elements)
     *       The numerical attributes of the engine. Read-only attribute.
     *
     * - CUDNN_ATTR_ENGINE_LAYOUT_INFO (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element)
     *       The preferred tensor layouts of the engine. Descriptor type
     *       CUDNN_BACKEND_LAYOUT_INFO_DESCRIPTOR. Read-only attribute.
     *
     * - CUDNN_ATTR_ENGINE_BEHAVIOR_NOTE (CUDNN_TYPE_BEHAVIOR_NOTE, 0+ elements)
     *       The behavior attributes of the engine. Read-only attribute.
     *
     * - CUDNN_ATTR_ENGINE_SM_COUNT_TARGET (CUDNN_TYPE_INT32, 1 element)
     *       The number of SMs to target. Valid values are between 0 and the
     *       number of SMs on the device, where 0 is default meaning all the SMs
     *       will be used. Optional attribute.
     *
     * - CUDNN_ATTR_ENGINE_DEVICEPROP (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element)
     *       The descriptor of the device that this engine descriptor targets.
     *       Descriptor type CUDNN_BACKEND_DEVICEPROP_DESCRIPTOR. Optional attribute.
     *
     * Finalization:
     *   CUDNN_STATUS_SUCCESS - The descriptor was finalized successfully.
     *   CUDNN_STATUS_NOT_SUPPORTED - The descriptor attribute set is not supported
     *       by the current version of cuDNN. For example, the value of
     *       CUDNN_ATTR_ENGINE_GLOBAL_INDEX is not in a valid range.
     *   CUDNN_STATUS_BAD_PARAM - The descriptor attribute set is inconsistent or
     *       in an unexpected state. For example, the operation graph descriptor set
     *       is not already finalized.
     */
    CUDNN_BACKEND_ENGINE_DESCRIPTOR                                = 2, /**< Engine (kernel grouping) to compute an operation graph. @since cuDNN 9.0.0 */
    /**
     * @brief Consists of an engine descriptor and an array of knob choice descriptors.
     *
     *
     * Created with cudnnBackendCreateDescriptor(CUDNN_BACKEND_ENGINECFG_DESCRIPTOR, &desc);
     * the cuDNN backend engine configuration descriptor consists of an engine
     * descriptor and an array of knob choice descriptors. Users can query from
     * engine config information about intermediates: computational intermediate
     * results that can be reused between executions.
     *
     * Supported attributes:
     *
     * - CUDNN_ATTR_ENGINECFG_ENGINE (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element)
     *       The backend engine. Descriptor type CUDNN_BACKEND_ENGINE_DESCRIPTOR.
     *       Required attribute.
     *
     * - CUDNN_ATTR_ENGINECFG_KNOB_CHOICES (CUDNN_TYPE_BACKEND_DESCRIPTOR, 0+ elements)
     *       The engine tuning knobs and choices. Descriptor type
     *       CUDNN_BACKEND_KNOB_CHOICE_DESCRIPTOR.
     *
     * - CUDNN_ATTR_ENGINECFG_INTERMEDIATE_INFO (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element)
     *       Information of the computational intermediate of this engine config.
     *       Descriptor type CUDNN_BACKEND_INTERMEDIATE_INFO_DESCRIPTOR.
     *       Read-only attribute. Currently unsupported. Placeholder for future
     *       implementation.
     *
     * - CUDNN_ATTR_ENGINECFG_WORKSPACE_SIZE (CUDNN_TYPE_INT64, 1 element)
     *       The size of the workspace buffer required to execute this engine config.
     *       Read-only attribute.
     *
     * Finalization:
     *   CUDNN_STATUS_SUCCESS - The descriptor was finalized successfully.
     *   CUDNN_STATUS_NOT_SUPPORTED - The descriptor attribute set is not supported
     *       by the current version of cuDNN. For example, the value knob.
     */
    CUDNN_BACKEND_ENGINECFG_DESCRIPTOR                             = 3, /**< Engine configuration: engine descriptor plus knob choices. @since cuDNN 9.0.0 */
    /**
     * @brief Allows users to obtain engine configurations ranked by performance heuristics.
     *
     *
     * Created with cudnnBackendCreateDescriptor(CUDNN_BACKEND_ENGINEHEUR_DESCRIPTOR, &desc);
     * the cuDNN backend engine heuristics descriptor allows users to obtain for
     * an operation graph engine configuration descriptors ranked by performance
     * according to cuDNN's heuristics.
     *
     * Supported attributes:
     *
     * - CUDNN_ATTR_ENGINEHEUR_OPERATION_GRAPH (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element)
     *       The operation graph for which heuristics result in a query.
     *       Required attribute.
     *
     * - CUDNN_ATTR_ENGINEHEUR_MODE (CUDNN_TYPE_HEUR_MODE, 1 element)
     *       The heuristic mode to query the result. Required attribute.
     *
     * - CUDNN_ATTR_ENGINEHEUR_RESULTS (CUDNN_TYPE_BACKEND_DESCRIPTOR, 0+ elements)
     *       The result of the heuristics query. Descriptor type
     *       CUDNN_BACKEND_ENGINECFG_DESCRIPTOR. Get-only attribute.
     *
     * - CUDNN_ATTR_ENGINEHEUR_SM_COUNT_TARGET (CUDNN_TYPE_INT32, 1 element)
     *       The number of SMs to target. Valid values are between 0 and the
     *       number of SMs on the device, where 0 is default meaning all the SMs
     *       will be used. Optional attribute.
     *
     * - CUDNN_ATTR_ENGINEHEUR_DEVICEPROP (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element)
     *       The descriptor of the device that this engine heuristics descriptor
     *       targets. Descriptor type CUDNN_BACKEND_DEVICEPROP_DESCRIPTOR.
     *       Optional attribute.
     *
     * Finalization:
     *   CUDNN_STATUS_SUCCESS - The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_ENGINEHEUR_DESCRIPTOR                            = 4, /**< Engine configurations ranked by performance via cuDNN heuristics. @since cuDNN 9.0.0 */
    /**
     * @brief Specifies an execution plan consisting of a handle, engine config, and optional intermediates.
     *
     *
     * Created with cudnnBackendCreateDescriptor(CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR, &desc);
     * the cuDNN backend execution plan descriptor allows the user to specify an
     * execution plan, consists of a cuDNN handle, an engine configuration, and
     * optionally an array of intermediates to compute.
     *
     * Supported attributes:
     *
     * - CUDNN_ATTR_EXECUTION_PLAN_HANDLE (CUDNN_TYPE_HANDLE, 1 element)
     *       A cuDNN handle. Required attribute.
     *
     * - CUDNN_ATTR_EXECUTION_PLAN_ENGINE_CONFIG (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element)
     *       An engine configuration to execute. Descriptor type
     *       CUDNN_BACKEND_ENGINECFG_DESCRIPTOR. Required attribute.
     *
     * - CUDNN_ATTR_EXECUTION_PLAN_RUN_ONLY_INTERMEDIATE_UIDS (CUDNN_TYPE_INT64, 0+ elements)
     *       Unique identifiers of intermediates to compute. Optional attribute.
     *       If set, the execution plan will only compute the specified intermediate
     *       and not any of the output tensors on the operation graph in the engine
     *       configuration.
     *
     * - CUDNN_ATTR_EXECUTION_PLAN_COMPUTED_INTERMEDIATE_UIDS (CUDNN_TYPE_INT64, 0+ elements)
     *       Unique identifiers of precomputed intermediates. Optional attribute.
     *       If set, the plan will expect and use pointers for each intermediate in
     *       the variant pack descriptor during execution. Currently unsupported.
     *       Placeholder for future implementation.
     *
     * - CUDNN_ATTR_EXECUTION_PLAN_WORKSPACE_SIZE (CUDNN_TYPE_INT64, 1 element)
     *       The size of the workspace buffer required to execute this plan.
     *       Read-only attribute.
     *
     * - CUDNN_ATTR_EXECUTION_PLAN_JSON_REPRESENTATION (CUDNN_TYPE_CHAR, many elements)
     *       The JSON representation of the serialized execution plan. Serialization
     *       and deserialization can be done by getting and setting this attribute,
     *       respectively. Element count is the same as the size of a null-terminated
     *       string of the json representation of the execution plan.
     *
     * - CUDNN_ATTR_EXECUTION_PLAN_KERNEL_CACHE (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element)
     *       The kernel cache that the execution plan can refer to in order to
     *       accelerate the finalization for runtime fusion engines by reusing a
     *       previously compiled identical kernel implementation. Descriptor type
     *       CUDNN_BACKEND_KERNEL_CACHE_DESCRIPTOR.
     *
     * - CUDNN_ATTR_EXECUTION_PLAN_DEVICEPROP (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element)
     *       The descriptor of the device that this execution plan targets.
     *       Descriptor type CUDNN_BACKEND_DEVICEPROP_DESCRIPTOR. Optional attribute.
     *
     * Finalization:
     *   CUDNN_STATUS_SUCCESS - The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR                        = 5, /**< Finalized execution plan: engine config, workspace size, optional kernel cache. @since cuDNN 9.0.0 */
    /**
     * @brief Read-only descriptor containing information about an execution intermediate.
     *
     *
     * Created with cudnnBackendCreateDescriptor(CUDNN_BACKEND_INTERMEDIATE_INFO_DESCRIPTOR, &desc);
     * the cuDNN backend intermediate descriptor is a read-only descriptor that
     * contains information about an execution intermediate. An execution
     * intermediate is some intermediate computation for an engine config in
     * device memory that can be reused between plan execution to amortize the
     * kernel. Each intermediate is identified by a unique ID. Users can query
     * for the device memory size of the intermediate. An intermediate can depend
     * on the data of one or more tensors identified by the tensor UIDs or one
     * more attribute of the operation graph.
     *
     * This is a read-only descriptor. Users cannot set the descriptor attributes
     * or finalize the descriptor. User query for a finalized descriptor from an
     * engine config descriptor.
     *
     * Supported attributes:
     *
     * - CUDNN_ATTR_INTERMEDIATE_INFO_UNIQUE_ID (CUDNN_TYPE_INT64, 1 element)
     *       A unique identifier of the intermediate. Read-only attribute.
     *
     * - CUDNN_ATTR_INTERMEDIATE_INFO_SIZE (CUDNN_TYPE_INT64, 1 element)
     *       The required device memory size for the intermediate. Read-only attribute.
     *
     * - CUDNN_ATTR_INTERMEDIATE_INFO_DEPENDENT_DATA_UIDS (CUDNN_TYPE_INT64, 0+ elements)
     *       UID of tensors on which the intermediate depends. Read-only attribute.
     *
     * - CUDNN_ATTR_INTERMEDIATE_INFO_DEPENDENT_ATTRIBUTES
     *       Currently unsupported. Placeholder for future implementation.
     *
     * Finalization:
     *   User does not finalize this descriptor. cudnnBackendFinalize(desc) with
     *   a backend intermediate descriptor returns CUDNN_STATUS_NOT_SUPPORTED.
     */
    CUDNN_BACKEND_INTERMEDIATE_INFO_DESCRIPTOR                     = 6, /**< Read-only info about a reusable execution intermediate. @since cuDNN 9.0.0 */
    /**
     * @brief Consists of the type and value of a performance knob setting.
     *
     *
     * Created with cudnnBackendCreateDescriptor(CUDNN_BACKEND_KNOB_CHOICE_DESCRIPTOR, &desc);
     * the cuDNN backend knob choice descriptor consists of the type of knobs to
     * be set and the value to which the knob is set.
     *
     * Supported attributes:
     *
     * - CUDNN_ATTR_KNOB_CHOICE_KNOB_TYPE (CUDNN_TYPE_KNOB_TYPE, 1 element)
     *       The type of knobs to be set. Required attribute.
     *
     * - CUDNN_ATTR_KNOB_CHOICE_KNOB_VALUE (CUDNN_TYPE_INT64, 1 element)
     *       The value of the knobs to be set. Required attribute.
     *
     * Finalization:
     *   CUDNN_STATUS_SUCCESS - The knob choice descriptor was finalized successfully.
     */
    CUDNN_BACKEND_KNOB_CHOICE_DESCRIPTOR                           = 7, /**< Type and value of an engine performance tuning knob. @since cuDNN 9.0.0 */
    /**
     * @brief 
     *
     * 9. CUDNN_BACKEND_KNOB_INFO_DESCRIPTOR
     *
     */
    CUDNN_BACKEND_KNOB_INFO_DESCRIPTOR                             = 8, /**< Read-only info about an engine knob: type, min/max, and stride. @since cuDNN 9.0.0 */
    /**
     * @brief Provides information on the preferred layout for a tensor.
     *
     *
     * Created with descriptor type value CUDNN_BACKEND_LAYOUT_INFO_DESCRIPTOR,
     * cuDNN backend layout info descriptor provides information on the preferred
     * layout for a tensor.
     *
     * Supported attributes:
     *
     * - CUDNN_ATTR_LAYOUT_INFO_TENSOR_UID (CUDNN_TYPE_INT64, 1 element)
     *       The UID of the tensor. Read-only attribute.
     *
     * - CUDNN_ATTR_LAYOUT_INFO_TYPES (CUDNN_TYPE_LAYOUT_TYPE, 0+ elements)
     *       The preferred layout of the tensor (cudnnBackendLayoutType_t).
     *       Read-only attribute.
     *
     * Finalization:
     *   This descriptor is read-only; it is retrieved and finalized from a cuDNN
     *   backend engine configuration descriptor. Users cannot set its attribute
     *   or finalize it.
     */
    CUDNN_BACKEND_LAYOUT_INFO_DESCRIPTOR                           = 9, /**< Read-only info on the preferred memory layout for a tensor. @since cuDNN 9.0.0 */
    /**
     * @brief Specifies an operation node for forward convolution.
     *
     *
     * Created with cudnnBackendCreateDescriptor(CUDNN_BACKEND_OPERATION_CONVOLUTION_FORWARD_DESCRIPTOR, &desc);
     * the cuDNN backend convolution forward operation descriptor specifies an
     * operation node for forward convolution to compute the response tensor y of
     * image tensor x convoluted with filter tensor w with output scaling alpha
     * and residual add with beta scaling. That is, the equation:
     *   y = alpha * (w * x) + beta * y
     * where * is the convolution operator in the forward direction.
     *
     * Supported attributes (prefix CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_):
     *
     * - CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_ALPHA (CUDNN_TYPE_FLOAT or CUDNN_TYPE_DOUBLE, 1+ elements)
     *       The alpha value. Required to be set before finalization.
     *
     * - CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_BETA (CUDNN_TYPE_FLOAT or CUDNN_TYPE_DOUBLE, 1+ elements)
     *       The beta value. Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_CONV_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element)
     *       The convolution operator descriptor. Descriptor type
     *       CUDNN_BACKEND_CONVOLUTION_DESCRIPTOR. Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_W (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element)
     *       The convolution filter tensor descriptor. Descriptor type
     *       CUDNN_BACKEND_TENSOR_DESCRIPTOR. Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_X (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element)
     *       The image tensor descriptor. Descriptor type
     *       CUDNN_BACKEND_TENSOR_DESCRIPTOR. Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_Y (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element)
     *       The response tensor descriptor. Descriptor type
     *       CUDNN_BACKEND_TENSOR_DESCRIPTOR. Required attribute.
     *
     * Notes on tensor dimension binding during finalization:
     *   The CUDNN_ATTR_CONVOLUTION_SPATIAL_DIMS attribute of
     *   CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_CONV_DESC is the number of
     *   spatial dimension of the convolution. The number of dimensions for
     *   tensor X, W, and Y must be larger than the number of spatial dimensions
     *   by 2 or 3 depending on how users choose to specify the convolution tensors.
     *
     *   If the number of tensor dimension is the number of spatial dimensions plus 2:
     *     - X tensor dimension and stride arrays are [N, GC, ...]
     *     - W tensor dimension and stride arrays are [GK, C, ...]
     *     - Y tensor dimension and stride arrays are [N, GK, ...]
     *   Where the ellipsis ... are shorthand for spatial dimensions of each tensor,
     *   G is the number of convolution groups, and C and K are the number of input
     *   and output feature maps per group. In this interpretation, it is assumed
     *   that the memory layout for each group is packed.
     *   cudnnBackendFinalize() asserts the tensors dimensions and strides are
     *   consistent with this interpretation or it returns CUDNN_STATUS_BAD_PARAM.
     *
     * Finalization:
     *   CUDNN_STATUS_BAD_PARAM - Invalid or inconsistent attribute values are
     *       encountered. For example, the X, W, and Y tensors do not constitute
     *       a valid convolution operation under the convolution operator.
     *   CUDNN_STATUS_SUCCESS - The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_OPERATION_CONVOLUTION_FORWARD_DESCRIPTOR         = 10, /**< Forward convolution: y = alpha * conv(w, x) + beta * y. @since cuDNN 9.0.0 */
    /**
     * @brief Specifies an operation node for convolution backward filter.
     *
     *
     * Created with cudnnBackendCreateDescriptor(CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_FILTER_DESCRIPTOR, &desc);
     * the cuDNN backend convolution backward filter operation descriptor
     * specifies an operation node for convolution backward filter to compute the
     * gradient of filter dw with image tensor x and gradient of response dy with
     * output alpha scaling and residue add with beta scaling. That is, the equation:
     *   dx = alpha * (x ~* dy) + beta * dx
     * where ~* denotes the convolution backward filter operator.
     *
     * Supported attributes (prefix CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_):
     *
     * - CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_ALPHA (CUDNN_TYPE_FLOAT or CUDNN_TYPE_DOUBLE, 1+ elements)
     *       The alpha value. Required attribute. Required to be set before finalization.
     *
     * - CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_BETA (CUDNN_TYPE_FLOAT or CUDNN_TYPE_DOUBLE, 1+ elements)
     *       The beta value. Required attribute. Required to be set before finalization.
     *
     * - CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_CONV_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element)
     *       The convolution operator descriptor. Descriptor type
     *       CUDNN_BACKEND_CONVOLUTION_DESCRIPTOR. Required attribute. Required to
     *       be set before finalization.
     *
     * - CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_DW (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element)
     *       The convolution filter tensor descriptor. Descriptor type
     *       CUDNN_BACKEND_TENSOR_DESCRIPTOR. Required attribute. Required to be
     *       set before finalization.
     *
     * - CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_X (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element)
     *       The image gradient tensor descriptor. Descriptor type
     *       CUDNN_BACKEND_TENSOR_DESCRIPTOR. Required attribute. Required to be
     *       set before finalization.
     *
     * - CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_DY (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element)
     *       The response gradient tensor descriptor. Descriptor type
     *       CUDNN_BACKEND_TENSOR_DESCRIPTOR. Required attribute. Required to be
     *       set before finalization.
     *
     * Notes on tensor dimension binding during finalization:
     *   In finalizing the convolution operation, the tensor dimensions of the
     *   tensor X, DW, and DY are bound based on the same interpretations as the
     *   X, W, and Y tensor dimensions described in the
     *   CUDNN_BACKEND_OPERATION_CONVOLUTION_FORWARD_DESCRIPTOR section.
     *
     * Finalization:
     *   CUDNN_STATUS_BAD_PARAM - Invalid or inconsistent attribute values are
     *       encountered. For example, the X, DW, and DY tensors do not constitute
     *       a valid convolution operation under the convolution operator.
     *   CUDNN_STATUS_SUCCESS - The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_FILTER_DESCRIPTOR = 11, /**< Backward filter convolution: computes dw from x and dy. @since cuDNN 9.0.0 */
    /**
     * @brief Specifies an operation node for convolution backward data.
     *
     *
     * Created with cudnnBackendCreateDescriptor(CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_DATA_DESCRIPTOR, &desc);
     * the cuDNN backend convolution backward data operation descriptor specifies
     * an operation node for convolution backward data to compute the gradient of
     * input data dx with filter tensor w and gradient of response dy with output
     * alpha scaling and residue add with beta scaling. That is, the equation:
     *   dx = alpha * (w _* dy) + beta * dx
     * where _* denotes the convolution backward data operator.
     *
     * Supported attributes (prefix CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_):
     *
     * - CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_ALPHA (CUDNN_TYPE_FLOAT or CUDNN_TYPE_DOUBLE, 1+ elements)
     *       The alpha value. Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_BETA (CUDNN_TYPE_FLOAT or CUDNN_TYPE_DOUBLE, 1+ elements)
     *       The beta value. Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_CONV_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element)
     *       The convolution operator descriptor. Descriptor type
     *       CUDNN_BACKEND_CONVOLUTION_DESCRIPTOR. Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_W (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element)
     *       The convolution filter tensor descriptor. Descriptor type
     *       CUDNN_BACKEND_TENSOR_DESCRIPTOR. Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_DX (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element)
     *       The image gradient tensor descriptor. Descriptor type
     *       CUDNN_BACKEND_TENSOR_DESCRIPTOR. Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_DY (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element)
     *       The response gradient tensor descriptor. Descriptor type
     *       CUDNN_BACKEND_TENSOR_DESCRIPTOR. Required attribute.
     *
     * Notes on tensor dimension binding during finalization:
     *   In finalizing the convolution operation, the tensor dimensions of the
     *   tensor DX, W, and DY are bound based on the same interpretations as the
     *   X, W, and Y tensor dimensions described in the
     *   CUDNN_BACKEND_OPERATION_CONVOLUTION_FORWARD_DESCRIPTOR section.
     *
     * Finalization:
     *   CUDNN_STATUS_BAD_PARAM - Invalid or inconsistent attribute values are
     *       encountered. For example, the DX, W, and DY tensors do not constitute
     *       a valid convolution operation under the convolution operator.
     *   CUDNN_STATUS_SUCCESS - The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_DATA_DESCRIPTOR   = 12, /**< Backward data convolution: computes dx from w and dy. @since cuDNN 9.0.0 */
    /**
     * @brief Represents a pointwise operation: Y = op(alpha1 * X) or Y = op(alpha1 * X, alpha2 * B).
     *
     *
     * Represents a pointwise operation that implements the equation
     * Y = op(alpha1 * X) or Y = op(alpha1 * X, alpha2 * B) depending on the
     * operation type. The actual type of operation represented by op() above
     * depends on the CUDNN_ATTR_OPERATION_POINTWISE_PW_DESCRIPTOR attribute in
     * the descriptor. This operation descriptor supports operations with
     * single-input single-output.
     *
     * For a list of supported operations, refer to the cudnnPointwiseMode_t section.
     *
     * For dual-input pointwise operations, broadcasting is assumed when a tensor
     * dimension in one of the tensors is 1 while the other tensors corresponding
     * dimension is not 1.
     *
     * For three-input single-output pointwise operations, we do not support
     * broadcasting in any tensor.
     *
     * This opaque struct can be created with
     * cudnnBackendCreateDescriptor(CUDNN_BACKEND_OPERATION_POINTWISE_DESCRIPTOR).
     *
     * Supported attributes:
     *
     * - CUDNN_ATTR_OPERATION_POINTWISE_PW_DESCRIPTOR
     *       Sets the descriptor containing the mathematical settings of the
     *       pointwise operation. This attribute is required.
     *
     * - CUDNN_ATTR_OPERATION_POINTWISE_XDESC
     *       Sets the descriptor for the input tensor X. This attribute is required
     *       for pointwise mathematical functions or activation forward propagation
     *       computations.
     *
     * - CUDNN_ATTR_OPERATION_POINTWISE_BDESC
     *       If the operation requires two inputs, such as add or multiply, this
     *       attribute sets the second input tensor B. If the operation requires
     *       only 1 input, this field is not used and should not be set.
     *
     * - CUDNN_ATTR_OPERATION_POINTWISE_YDESC
     *       Sets the descriptor for the output tensor Y. This attribute is
     *       required for pointwise mathematical functions or activation forward
     *       propagation computations.
     *
     * - CUDNN_ATTR_OPERATION_POINTWISE_TDESC
     *       Sets the descriptor for the tensor T. This attribute is required for
     *       CUDNN_ATTR_POINTWISE_MODE set to CUDNN_POINTWISE_BINARY_SELECT and
     *       acts as the mask based on which the selection is done.
     *
     * - CUDNN_ATTR_OPERATION_POINTWISE_ALPHA1
     *       Sets the scalar alpha1 value in the equation. Can be in float or half.
     *       This attribute is optional, if not set, the default value is 1.0.
     *
     * - CUDNN_ATTR_OPERATION_POINTWISE_ALPHA2
     *       If the operation requires 2 inputs, such as add or multiply. This
     *       attribute sets the scalar alpha2 value in the equation. Can be in
     *       float or half. This attribute is optional, if not set, the default
     *       value is 1.0. If the operation requires only 1 input, this field is
     *       not used and should not be set.
     *
     * - CUDNN_ATTR_OPERATION_POINTWISE_DXDESC
     *       Sets the descriptor for the output tensor dX. This attribute is
     *       required for pointwise activation back propagation computations.
     *
     * - CUDNN_ATTR_OPERATION_POINTWISE_DYDESC
     *       Sets the descriptor for the input tensor dY. This attribute is
     *       required for pointwise activation back propagation computations.
     *
     * Finalization:
     *   CUDNN_STATUS_BAD_PARAM - Invalid or inconsistent attribute values are
     *       encountered. Some examples include:
     *       - The number of dimensions do not match between the input and output tensors.
     *       - The input/output tensor dimensions do not agree with the above
     *         described automatic broadcasting rules.
     *   CUDNN_STATUS_SUCCESS - The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_OPERATION_POINTWISE_DESCRIPTOR                   = 13, /**< Pointwise operation: Y = op(alpha1*X) or Y = op(alpha1*X, alpha2*B). @since cuDNN 9.0.0 */
    /**
     * @brief Represents an operation that generates per-channel statistics.
     *
     *
     * Represents an operation that will generate per-channel statistics. The
     * specific statistics that will be generated depends on the
     * CUDNN_ATTR_OPERATION_GENSTATS_MODE attribute in the descriptor. Currently,
     * only CUDNN_GENSTATS_SUM_SQSUM is supported for the
     * CUDNN_ATTR_OPERATION_GENSTATS_MODE. It will generate the sum and quadratic
     * sum of per-channel elements of the input tensor x. The output dimension
     * should be all 1 except the C dimension. Also, the C dimension of outputs
     * should equal the C dimension of the input. This opaque struct can be
     * created with cudnnBackendCreateDescriptor(CUDNN_BACKEND_OPERATION_GEN_STATS_DESCRIPTOR).
     *
     * Supported attributes:
     *
     * - CUDNN_ATTR_OPERATION_GENSTATS_MODE
     *       Sets the CUDNN_TYPE_GENSTATS_MODE of the operation. This attribute
     *       is required.
     *
     * - CUDNN_ATTR_OPERATION_GENSTATS_MATH_PREC
     *       The math precision of the computation. This attribute is required.
     *
     * - CUDNN_ATTR_OPERATION_GENSTATS_XDESC
     *       Sets the descriptor for the input tensor X. This attribute is required.
     *
     * - CUDNN_ATTR_OPERATION_GENSTATS_SUMDESC
     *       Sets the descriptor for the output tensor sum. This attribute is required.
     *
     * - CUDNN_ATTR_OPERATION_GENSTATS_SQSUMDESC
     *       Sets the descriptor for the output tensor quadratic sum. This
     *       attribute is required.
     *
     * Finalization:
     *   CUDNN_STATUS_BAD_PARAM - Invalid or inconsistent attribute values are
     *       encountered. Some examples include:
     *       - The number of dimensions do not match between the input and output tensors.
     *       - The input/output tensor dimensions do not agree with the above description.
     *   CUDNN_STATUS_SUCCESS - The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_OPERATION_GEN_STATS_DESCRIPTOR                   = 14, /**< Generates per-channel statistics (sum and sum-of-squares). @since cuDNN 9.0.0 */
    /**
     * @brief Describes an operation graph of one or more operations connected by virtual tensors.
     *
     *
     * Created with descriptor type value CUDNN_BACKEND_OPERATIONGRAPH_DESCRIPTOR,
     * cuDNN backend operation graph descriptor describes an operation graph, a small
     * network of one or more operations connected by virtual tensors. Operation graph
     * defines users' computation case or mathematical expression that they wish to compute.
     *
     * Supported attributes:
     * - CUDNN_ATTR_OPERATIONGRAPH_HANDLE (CUDNN_TYPE_HANDLE, 1 element)
     *     A cuDNN handle.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATIONGRAPH_OPS (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1+ elements of type CUDNN_BACKEND_OPERATION_*_DESCRIPTOR)
     *     Operation nodes to form the operation graph.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATIONGRAPH_ENGINE_GLOBAL_COUNT (CUDNN_TYPE_INT64, 1 element)
     *     The number of engines to support the operation graph.
     *     Read-only attribute.
     *
     * - CUDNN_ATTR_OPERATIONGRAPH_ENGINE_SUPPORTED_COUNT (CUDNN_TYPE_INT64, 1 element)
     *     The number of engines that support the operation graph.
     *     Read-only attribute.
     *     Currently unsupported. Placeholder for future implementation.
     *
     * - CUDNN_ATTR_OPERATIONGRAPH_IS_DYNAMIC_SHAPE_ENABLED (CUDNN_TYPE_BOOLEAN, 1 element)
     *     Whether dynamic shape is enabled for the operation graph. The rest of the
     *     backend API will treat the graph as a dynamic shape graph and enable this feature.
     *
     * Finalization:
     *   CUDNN_STATUS_BAD_PARAM - An invalid attribute value was encountered. Some examples include:
     *     - One of the backend descriptors in CUDNN_ATTR_OPERATIONGRAPH_OPS is not finalized.
     *     - The value CUDNN_ATTR_OPERATIONGRAPH_HANDLE is not a valid cuDNN handle.
     *   CUDNN_STATUS_NOT_SUPPORTED - An unsupported attribute value was encountered.
     *     For example, the combination of operations of attribute CUDNN_ATTR_OPERATIONGRAPH_OPS
     *     is not supported.
     *   CUDNN_STATUS_SUCCESS - The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_OPERATIONGRAPH_DESCRIPTOR                        = 15, /**< Operation graph: a DAG of operations connected by virtual tensors. @since cuDNN 9.0.0 */
    /**
     * @brief Sets up pointers to device buffers for non-virtual tensors, workspace, and computation intermediates.
     *
     *
     * Created with cudnnBackendCreateDescriptor(CUDNN_BACKEND_VARIANT_PACK_DESCRIPTOR, &desc);
     * the cuDNN backend variant pack plan allows users to set up pointers to device buffers
     * to various non-virtual tensors, identified by unique identifiers, of the operation graph,
     * workspace, and computation intermediates.
     *
     * Supported attributes:
     * - CUDNN_ATTR_VARIANT_PACK_UNIQUE_IDS (CUDNN_TYPE_INT64, 0+ elements)
     *     A unique identifier of tensor for each data pointer.
     *     Required attribute.
     *
     * - CUDNN_ATTR_VARIANT_PACK_DATA_POINTERS (CUDNN_TYPE_VOID_PTR, 0+ elements)
     *     Tensor data device pointers.
     *     Required attribute.
     *
     * - CUDNN_ATTR_VARIANT_PACK_INTERMEDIATES (CUDNN_TYPE_VOID_PTR, 0+ elements)
     *     Intermediate device pointers.
     *     Currently unsupported. Placeholder for future implementation.
     *
     * - CUDNN_ATTR_VARIANT_PACK_WORKSPACE (CUDNN_TYPE_VOID_PTR, 1 element)
     *     Workspace to device pointer.
     *     Required attribute.
     *
     * Finalization:
     *   CUDNN_STATUS_SUCCESS - The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_VARIANT_PACK_DESCRIPTOR                          = 16, /**< Binds device pointers to non-virtual tensors, workspace, and intermediates. @since cuDNN 9.0.0 */
    /**
     * @brief Specifies the memory storage of a generic tensor.
     *
     *
     * Created with cudnnBackendCreateDescriptor(CUDNN_BACKEND_TENSOR_DESCRIPTOR, &desc);
     * the cuDNN backend tensor allows users to specify the memory storage of a generic tensor.
     * A tensor is identified by a unique identifier and described by its data type, its data
     * byte-alignment requirements, and the extents and strides of its dimensions. Optionally,
     * a tensor element can be vector in one of its dimensions. A tensor can also be set to be
     * virtual when it is an intermediate variable in a computation graph and not mapped to
     * physical global memory storage.
     *
     * Supported attributes:
     * - CUDNN_ATTR_TENSOR_UNIQUE_ID (CUDNN_TYPE_INT64, 1 element)
     *     An integer that uniquely identifies the tensor.
     *     Required attribute.
     *
     * - CUDNN_ATTR_TENSOR_DATA_TYPE (CUDNN_TYPE_DATA_TYPE, 1 element)
     *     Data type of tensor.
     *     Required attribute.
     *
     * - CUDNN_ATTR_TENSOR_BYTE_ALIGNMENT (CUDNN_TYPE_INT64, 1 element)
     *     Byte alignment of pointers for this tensor.
     *     Required attribute.
     *
     * - CUDNN_ATTR_TENSOR_DIMENSIONS (CUDNN_TYPE_INT64, at most CUDNN_MAX_DIMS elements)
     *     Tensor dimensions.
     *     Required attribute.
     *
     * - CUDNN_ATTR_TENSOR_STRIDES (CUDNN_TYPE_INT64, at most CUDNN_MAX_DIMS elements)
     *     Tensor strides.
     *     Required attribute.
     *
     * - CUDNN_ATTR_TENSOR_VECTOR_COUNT (CUDNN_TYPE_INT64, 1 element)
     *     Size of vectorization.
     *     Default value is 1.
     *
     * - CUDNN_ATTR_TENSOR_VECTORIZED_DIMENSION (CUDNN_TYPE_INT64, 1 element)
     *     Index of the vectorized dimension.
     *     Required to be set before finalization if CUDNN_ATTR_TENSOR_VECTOR_COUNT is set
     *     to a value different than its default; otherwise it's ignored.
     *
     * - CUDNN_ATTR_TENSOR_IS_VIRTUAL (CUDNN_TYPE_BOOLEAN, 1 element)
     *     Indicates whether the tensor is virtual. A virtual tensor is an intermediate tensor
     *     in the operation graph that exists in transient and not read from or written to in
     *     global device memory.
     *     Default value is false.
     *
     * - CUDNN_ATTR_TENSOR_RAGGED_OFFSET_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element)
     *     A ragged tensor, that is, a tensor with nested variable length lists as inner
     *     dimensions, will have another tensor called the ragged offset descriptor that
     *     contains offsets in memory to the next variable length list.
     *     Default value is None.
     *
     * - CUDNN_ATTR_TENSOR_RAGGED_OFFSET_MULTIPLIER (CUDNN_TYPE_INT64, 1 element)
     *     Unit size of the offsets stored in CUDNN_ATTR_TENSOR_RAGGED_OFFSET_DESC,
     *     as a multiple of the tensor's element size (*not* in bytes).
     *     Consumers of the ragged offset tensor multiply its values by this
     *     multiplier before use. Only a limited number of engines support this attribute.
     *     If set, CUDNN_ATTR_TENSOR_RAGGED_OFFSET_DESC must also be set.
     *     Default value is 1 (meaning the ragged offset tensor is in units of elements
     *     of the tensor's data type).
     *
     * Finalization:
     *   CUDNN_STATUS_BAD_PARAM - An invalid attribute value was encountered. Some examples include:
     *     - Any of the tensor dimensions or strides is not positive.
     *     - The value of the tensor alignment attribute is not divisible by the size of the data type.
     *   CUDNN_STATUS_NOT_SUPPORTED - An unsupported attribute value was encountered. Some examples include:
     *     - The data type attribute is CUDNN_DATA_INT8x4, CUDNN_DATA_UINT8x4, or CUDNN_DATA_INT8x32.
     *     - The data type attribute is CUDNN_DATA_INT8 and CUDNN_ATTR_TENSOR_VECTOR_COUNT value is not 1, 4, or 32.
     *   CUDNN_STATUS_SUCCESS - The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_TENSOR_DESCRIPTOR                                = 17, /**< Tensor: data type, dimensions, strides, alignment, unique ID, virtual flag. @since cuDNN 9.0.0 */
    /**
     * @brief Specifies metadata needed for the matmul operation.
     *
     *
     * Created with cudnnBackendCreateDescriptor(CUDNN_BACKEND_MATMUL_DESCRIPTOR, &desc);
     * the cuDNN backend matmul descriptor specifies any metadata needed for the matmul operation.
     *
     * Supported attributes:
     * - CUDNN_ATTR_MATMUL_COMP_TYPE (CUDNN_TYPE_DATA_TYPE, 1 element)
     *     The compute precision used for the matmul operation.
     *     Required attribute.
     *
     * Finalization:
     *   Return values of cudnnBackendFinalize(desc) where desc is a cuDNN backend matmul descriptor:
     *   CUDNN_STATUS_SUCCESS - The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_MATMUL_DESCRIPTOR                                = 18, /**< Matrix multiply config: compute type and padding value. @since cuDNN 9.0.0 */
    /**
     * @brief Specifies an operation node for matmul to compute C = A * B.
     *
     *
     * Created with cudnnBackendCreateDescriptor(CUDNN_BACKEND_OPERATION_MATMUL_DESCRIPTOR, &desc);
     * the cuDNN backend matmul operation descriptor specifies an operation node for matmul to
     * compute the matrix product C by multiplying Matrix A and Matrix B, as shown in the
     * following equation: C=AB
     *
     * When using the matmul operation, the matrices are expected to be at least rank-2 tensors.
     * The last two dimensions are expected to correspond to either M, K or N. All the preceding
     * dimensions are interpreted as batch dimensions. If there are zero batch dimensions then
     * the requirements are as follows:
     *
     *   Zero Batch Dimensions:
     *     Single Matmul: A is M x K, B is K x N, C is M x N.
     *
     *   Single Batch Dimension:
     *     Single Matmul: A is 1 x M x K, B is 1 x K x N, C is 1 x M x N.
     *     Batch Matmul:  A is B x M x K, B is B x K x N, C is B x M x N.
     *     Broadcast A:   A is (B/c) x M x K, B is B x K x N, C is B x M x N.
     *     Broadcast B:   A is B x M x K, B is (B/c) x K x N, C is B x M x N.
     *
     *   Where:
     *     B indicates the batch size.
     *     M is the number of rows of the Matrix A.
     *     K is the number of columns of the input Matrix A (which is the same as the number
     *       of rows as the input Matrix B).
     *     N is the number of columns of the input Matrix B.
     *     c is a constant integer and a factor of B.
     *
     *   If either the batch size of Matrix A or B is set to B/c, this indicates that the matrix
     *   will be broadcasted in the batch matmul. The resulting output Matrix C will be a tensor
     *   of B x M x N.
     *
     *   The above broadcasting convention is extended to all the batch dimensions. Concretely,
     *   for tensors with three batch dimensions:
     *     Multiple Batched Matmul: A is B1 x 1 x B3 x M x K, B is 1 x B2 x (B3/c) x K x N,
     *       C is B1 x B2 x B3 x M x N.
     *
     *   The functionality of having multiple batch dimensions allows you to have layouts where
     *   the batch is not packed at a single stride. This case is especially seen in multihead
     *   attention. c is only allowed to be B (leading to a batch dimension for 1) for matmul
     *   and matmul fusions. The other possible values of c are supported for Grouped Query
     *   Attention in the cuDNN Fused Flash Attention.
     *
     *   The addressing of the matrix elements from a given tensor can be specified using strides
     *   in the tensor descriptor. The strides represent the spacing between elements for each
     *   tensor dimension. Considering a matrix tensor A (B x M x N) with strides [BS, MS, NS],
     *   it indicates that the actual matrix element A[x, y, z] is found at
     *   (A_base_address + x * BS + y * MS + z * NS) from the linear memory space allocated for
     *   tensor A. With our current support, the innermost dimension must be packed, which
     *   requires either MS=1 or NS=1. Otherwise, there are no other technical constraints with
     *   regard to how the strides can be specified in a tensor descriptor as it should follow
     *   the aforementioned addressing formula and the strides as specified by the user.
     *
     *   This representation provides support for some common usages, such as leading dimension
     *   and matrix transpose as we will explain through the following examples.
     *
     *   1. The most basic case is a fully packed row-major batch matrix, without any
     *      consideration of leading dimension or transpose. In this case,
     *      BS = M*N, MS = N, and NS = 1.
     *   2. Matrix transpose can be achieved by exchanging the inner and outer dimensions
     *      using strides. Namely:
     *      a. To specify a non-transposed matrix: BS = M*N, MS = N, and NS = 1.
     *      b. To specify matrix transpose: BS = M*N, MS = 1, and NS = M.
     *   3. Leading dimension, a widely used concept in BLAS-like APIs, describes the inner
     *      dimension of the 2D array memory allocation (as opposed to the conceptual matrix
     *      dimension). It resembles the stride in a way that it defines the spacing between
     *      elements in the outer dimension. The most typical use cases where it shows difference
     *      from the matrix inner dimension is when the matrix is only part of the data in the
     *      allocated memory, addressing submatrices, or addressing matrices from an aligned
     *      memory allocation. Therefore, the leading dimension LDA in a column-major matrix A
     *      must satisfy LDA >= M, whereas in a row-major matrix A, it must satisfy LDA >= N.
     *      To transition from the leading dimension concept to using strides, this entails
     *      MS >= N and NS = 1 or MS = 1 and NS >= M. Keep in mind that, while these are some
     *      practical use cases, these inequalities do not impose technical constraints with
     *      respect to an acceptable specification of the strides.
     *
     *   Other commonly used GEMM features, such as alpha/beta output blending, can also be
     *   achieved using this matmul operation along with other pointwise operations.
     *
     * Supported attributes:
     * - CUDNN_ATTR_OPERATION_MATMUL_ADESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     The Matrix A descriptor.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_MATMUL_BDESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     The Matrix B descriptor.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_MATMUL_CDESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     The Matrix C descriptor.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_MATMUL_IRREGULARLY_STRIDED_BATCH_COUNT (CUDNN_TYPE_INT64, 1 element)
     *     Number of matmul operations to perform in the batch on matrix.
     *     Default value is 1.
     *
     * - CUDNN_ATTR_OPERATION_MATMUL_GEMM_M_OVERRIDE_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     The tensor gemm_m_override descriptor. Allows you to override the M dimension of a
     *     batch matmul through this tensor. It is only supported as documented in the Fused
     *     Attention fprop, Fused Attention bprop, Fused Flash Attention fprop, and Fused Flash
     *     Attention bprop sections.
     *     Optional attribute.
     *
     * - CUDNN_ATTR_OPERATION_MATMUL_GEMM_N_OVERRIDE_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     The tensor gemm_n_override descriptor. Allows you to override the N dimension of a
     *     batch matmul through this tensor. It is only supported as documented in the Fused
     *     Attention fprop, Fused Attention bprop, Fused Flash Attention fprop, and Fused Flash
     *     Attention bprop sections.
     *     Optional attribute.
     *
     * - CUDNN_ATTR_OPERATION_MATMUL_GEMM_K_OVERRIDE_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     The tensor gemm_k_override descriptor. Allows you to override the K dimension of a
     *     batch matmul through this tensor. It is only supported as documented in the Fused
     *     Attention fprop, Fused Attention bprop, Fused Flash Attention fprop, and Fused Flash
     *     Attention bprop sections.
     *     Optional attribute.
     *
     * - CUDNN_ATTR_OPERATION_MATMUL_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_MATMUL_DESCRIPTOR)
     *     The matmul operation descriptor.
     *     Required attribute.
     *
     * Finalization:
     *   In the finalization of the matmul operation, the tensor dimensions of the Matrices
     *   A, B, and C will be checked to ensure that they satisfy the requirements of matmul.
     *
     *   CUDNN_STATUS_NOT_SUPPORTED - An unsupported attribute value was encountered. For
     *     example, if not all of the Matrices A, B, and C are at least rank-2 tensors.
     *   CUDNN_STATUS_BAD_PARAM - Invalid or inconsistent attribute values are encountered.
     *     Some examples include:
     *     - The CUDNN_ATTR_OPERATION_MATMUL_IRREGULARLY_STRIDED_BATCH_COUNT specified is a
     *       negative value.
     *     - The CUDNN_ATTR_OPERATION_MATMUL_IRREGULARLY_STRIDED_BATCH_COUNT and one or more
     *       of the batch sizes of the Matrices A, B, and C are not equal to one. That is to
     *       say there is a conflict where both irregularly and regularly strided batched matmul
     *       are specified, which is not a valid use case.
     *     - The dimensions of the Matrices A, B, and C do not satisfy the matmul requirements.
     *   CUDNN_STATUS_SUCCESS - The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_OPERATION_MATMUL_DESCRIPTOR                      = 19, /**< Matrix multiplication operation: C = A * B with optional overrides. @since cuDNN 9.0.0 */
    /**
     * @brief Specifies an operation node for the batch norm finalize operation.
     *
     *
     * Created with cudnnBackendCreateDescriptor(CUDNN_BACKEND_OPERATION_BN_FINALIZE_STATISTICS_DESCRIPTOR, &desc);
     * the cuDNN backend bn_finalize statistics operation descriptor specifies an operation node
     * for the batch norm finalize operation.
     *
     * In ResNet like models, a common technique to fuse batch norm with convolutions would
     * involve splitting the batch norm operation into three parts - genStats, finalize, and
     * apply (pointwise, scale, and bias). The genStats operation is usually fused with the
     * convolution operation that precedes the batch norm while the apply is fused with the
     * ReLU and convolution that follows the batch norm op. The batch norm finalize operation
     * is a buffer op between the two fusions that takes the batch norm scale, bias, sum, and
     * sqsum produced by the genStats operation as inputs and produces an equivalent scale and
     * bias as output. The equivalent scale and bias are then consumed in the apply phase.
     * Additionally, the bn_finalize operation also produces the running stats, mean, and
     * inverse standard deviation as outputs.
     *
     * Supported attributes:
     * - CUDNN_ATTR_OPERATION_BN_FINALIZE_STATS_MODE (CUDNN_TYPE_BN_FINALIZE_STATS_MODE, 1 element)
     *     Sets inference or training mode for the bn_finalize operation.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_BN_FINALIZE_MATH_PREC
     *     Math precision of the computation.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_BN_FINALIZE_Y_SUM_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Input sum tensor descriptor.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_BN_FINALIZE_Y_SQ_SUM_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Input square sum tensor descriptor.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_BN_FINALIZE_SCALE_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Batch norm input scale tensor descriptor.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_BN_FINALIZE_BIAS_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Batch norm input bias tensor descriptor.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_BN_FINALIZE_PREV_RUNNING_MEAN_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Batch norm input running mean descriptor.
     *     Optional attribute.
     *
     * - CUDNN_ATTR_OPERATION_BN_FINALIZE_PREV_RUNNING_VAR_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Batch norm input running variance descriptor.
     *     Optional attribute.
     *
     * - CUDNN_ATTR_OPERATION_BN_FINALIZE_UPDATED_RUNNING_MEAN_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Batch norm output running mean descriptor.
     *     Optional attribute.
     *
     * - CUDNN_ATTR_OPERATION_BN_FINALIZE_UPDATED_RUNNING_VAR_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Batch norm output running variance descriptor.
     *     Optional attribute.
     *
     * - CUDNN_ATTR_OPERATION_BN_FINALIZE_SAVED_MEAN_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Batch norm output saved mean tensor descriptor. This is computed from the sum input
     *     that's fed in from the preceding genStats operation. Storing out the saved mean helps
     *     avoid recomputation in the backpropagation phase.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_BN_FINALIZE_SAVED_INV_STD_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Batch norm output inverse standard deviation tensor descriptor. This is computed from
     *     the sum and sqm sums input that's fed in from the preceding genStats operation. Storing
     *     out the saved inv standard deviations helps avoid recomputation in the backpropagation phase.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_BN_FINALIZE_EQ_SCALE_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Output tensor descriptor for the equivalent scale tensor. The equivalent scale tensor
     *     is typically fed as input to the batch norm apply computation (pointwise, scale, and
     *     bias) that follows the batch norm finalize operation.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_BN_FINALIZE_EQ_BIAS_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Output tensor descriptor for the equivalent bias tensor. The equivalent bias tensor
     *     is typically fed as input to the batch norm apply computation (pointwise, scale, and
     *     bias) that follows the batch norm finalize operation.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_BN_FINALIZE_ACCUM_COUNT_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Scalar input tensor descriptor representing the number of elements accumulated over
     *     while calculating the sum and sqsum inputs. The count usually equals N*H*W in case
     *     of batch norm.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_BN_FINALIZE_EPSILON_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Scalar input tensor descriptor for the epsilon value used in batch norm variance
     *     calculation.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_BN_FINALIZE_EXP_AVERAGE_FACTOR_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Scalar input tensor descriptor for the exponential average value used in batch norm
     *     running stats calculation.
     *     Required attribute.
     */
    CUDNN_BACKEND_OPERATION_BN_FINALIZE_STATISTICS_DESCRIPTOR      = 20, /**< BN finalize: computes running stats, saved mean/invstd, eq scale/bias. @since cuDNN 9.0.0 */
    /**
     * @brief Specifies metadata for the reduction operation including math operation and compute data type.
     *
     *
     * Created with cudnnBackendCreateDescriptor(CUDNN_BACKEND_REDUCTION_DESCRIPTOR, &desc);
     * the cuDNN backend reduction descriptor specifies any metadata, including the math
     * operation and compute data type, needed for the reduction operation.
     *
     * Supported attributes:
     * - CUDNN_ATTR_REDUCTION_OPERATOR (CUDNN_TYPE_REDUCTION_OPERATOR_TYPE, 1 element)
     *     The math operation used for the reduction operation.
     *     Required attribute.
     *
     * - CUDNN_ATTR_REDUCTION_COMP_TYPE (CUDNN_TYPE_DATA_TYPE, 1 element)
     *     The compute precision used for the reduction operation.
     *     Required attribute.
     *
     * Finalization:
     *   CUDNN_STATUS_NOT_SUPPORTED - An unsupported attribute value was encountered. For
     *     example, CUDNN_ATTR_REDUCTION_OPERATOR is not set to either of
     *     CUDNN_REDUCE_TENSOR_ADD, CUDNN_REDUCE_TENSOR_MUL, CUDNN_REDUCE_TENSOR_MIN,
     *     or CUDNN_REDUCE_TENSOR_MAX.
     *   CUDNN_STATUS_SUCCESS - The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_REDUCTION_DESCRIPTOR                             = 21, /**< Reduction config: operator type (ADD/MUL/MIN/MAX/etc.) and compute type. @since cuDNN 9.0.0 */
    /**
     * @brief Represents a reduction operation node that reduces input tensor X to output tensor Y.
     *
     *
     * The cuDNN backend reduction operation descriptor represents an operation node that
     * implements reducing values of an input tensor X in one or more dimensions to get an
     * output tensor Y. The math operation and compute data type used for reducing tensor
     * values is specified via CUDNN_ATTR_OPERATION_REDUCTION_DESC.
     *
     * This operation descriptor can be created with
     * cudnnBackendCreateDescriptor(CUDNN_BACKEND_OPERATION_REDUCTION_DESCRIPTOR, &desc).
     *
     * The output tensor Y should be the size as that of input tensor X, except dimensions
     * where its size is 1.
     *
     * There is a special use case for Grouped Query Attention and Multi Query Attention in
     * cuDNN Fused Flash Attention where some dimensions in the output tensor Y can also be
     * factors of the corresponding dimensions in the input tensor X.
     *
     * Supported attributes:
     * - CUDNN_ATTR_OPERATION_REDUCTION_XDESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     The matrix X descriptor.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_REDUCTION_YDESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     The matrix Y descriptor.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_REDUCTION_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     The reduction operation descriptor.
     *     Required attribute.
     *
     * Finalization:
     *   In the finalization of the reduction operation, the dimensions of tensors X and Y are
     *   checked to ensure that they satisfy the requirements of the reduction operation.
     *
     *   CUDNN_STATUS_BAD_PARAM - Invalid or inconsistent attribute values are encountered.
     *     For example, the dimensions of the tensors X and Y do not satisfy the requirements
     *     of the reduction operation.
     *   CUDNN_STATUS_SUCCESS - The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_OPERATION_REDUCTION_DESCRIPTOR                   = 22, /**< Reduces input tensor values along one or more dimensions. @since cuDNN 9.0.0 */
    /**
     * @brief Batch normalization backward weights operation descriptor.
     *
     *
     * NOTE: This descriptor type (CUDNN_BACKEND_OPERATION_BN_BWD_WEIGHTS_DESCRIPTOR) is
     * listed in the cudnnBackendDescriptorType_t enumeration but does not have a dedicated
     * documentation section in the cuDNN backend API RST reference. The associated attributes
     * are defined in the cudnnBackendAttributeName_t enumeration with prefix
     * CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_ and include:
     *   - CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_MATH_PREC        (1620)
     *   - CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_MEAN_DESC        (1621)
     *   - CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_INVSTD_DESC      (1622)
     *   - CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_BN_SCALE_DESC    (1623)
     *   - CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_X_DESC           (1624)
     *   - CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_DY_DESC          (1625)
     *   - CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_DBN_SCALE_DESC   (1626)
     *   - CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_DBN_BIAS_DESC    (1627)
     *   - CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_EQ_DY_SCALE_DESC (1628)
     *   - CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_EQ_X_SCALE_DESC  (1629)
     *   - CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_EQ_BIAS          (1630)
     */
    CUDNN_BACKEND_OPERATION_BN_BWD_WEIGHTS_DESCRIPTOR              = 23, /**< BN backward weights: computes dScale, dBias, and equivalent gradients. @since cuDNN 9.0.0 */
    /**
     * @brief Specifies parameters for a resample operation (upsampling or downsampling).
     *
     *
     * Created with cudnnBackendCreateDescriptor(CUDNN_BACKEND_RESAMPLE_DESCRIPTOR, &desc);
     * the cuDNN backend resample descriptor specifies the parameters for a resample operation
     * (upsampling or downsampling) in both forward and backward propagation.
     *
     * Supported attributes:
     * - CUDNN_ATTR_RESAMPLE_MODE (CUDNN_TYPE_RESAMPLE_MODE, 1 element)
     *     Specifies mode of resampling, for example, average pool, nearest-neighbor, and so on.
     *     Default value is CUDNN_RESAMPLE_NEAREST.
     *
     * - CUDNN_ATTR_RESAMPLE_COMP_TYPE (CUDNN_TYPE_DATA_TYPE, 1 element)
     *     Compute data type for the resampling operator.
     *     Default value is CUDNN_DATA_FLOAT.
     *
     * - CUDNN_ATTR_RESAMPLE_NAN_PROPAGATION (CUDNN_TYPE_NAN_PROPAGATION, 1 element)
     *     Specifies a method by which to propagate NaNs.
     *     Default value is CUDNN_NOT_PROPAGATE_NAN.
     *
     * - CUDNN_ATTR_RESAMPLE_SPATIAL_DIMS (CUDNN_TYPE_INT64, 1 element)
     *     Specifies the number of spatial dimensions to perform the resampling over.
     *     Required attribute.
     *
     * - CUDNN_ATTR_RESAMPLE_PADDING_MODE (CUDNN_TYPE_PADDING_MODE, 1 element)
     *     Specifies which values to use for padding.
     *     Default value is CUDNN_ZERO_PAD.
     *
     * - CUDNN_ATTR_RESAMPLE_STRIDES (CUDNN_TYPE_INT64 or CUDNN_TYPE_FRACTION, at most CUDNN_MAX_DIMS - 2)
     *     Stride in each dimension for the kernel or filter.
     *     Required attribute.
     *
     * - CUDNN_ATTR_RESAMPLE_PRE_PADDINGS (CUDNN_TYPE_INT64 or CUDNN_TYPE_FRACTION, at most CUDNN_MAX_DIMS - 2)
     *     Padding added to the beginning of the input tensor in each dimension.
     *     Required attribute.
     *
     * - CUDNN_ATTR_RESAMPLE_POST_PADDINGS (CUDNN_TYPE_INT64 or CUDNN_TYPE_FRACTION, at most CUDNN_MAX_DIMS - 2)
     *     Padding added to the end of the input tensor in each dimension.
     *     Required attribute.
     *
     * - CUDNN_ATTR_RESAMPLE_WINDOW_DIMS (CUDNN_TYPE_INT64 or CUDNN_TYPE_FRACTION, at most CUDNN_MAX_DIMS - 2)
     *     Spatial dimensions of filter.
     *     Required attribute.
     *
     * Finalization:
     *   CUDNN_STATUS_NOT_SUPPORTED - An unsupported attribute value was encountered. Some
     *     examples include:
     *     - An elemCount argument for setting CUDNN_ATTR_RESAMPLE_WINDOW_DIMS,
     *       CUDNN_ATTR_RESAMPLE_STRIDES, CUDNN_ATTR_RESAMPLE_PRE_PADDINGS, and
     *       CUDNN_ATTR_RESAMPLE_POST_PADDINGS is not equal to the value set for
     *       CUDNN_ATTR_RESAMPLE_SPATIAL_DIMS.
     *     - CUDNN_ATTR_RESAMPLE_MODE is set to CUDNN_RESAMPLE_BILINEAR and any of the
     *       CUDNN_ATTR_RESAMPLE_WINDOW_DIMS are not set to 2.
     *   CUDNN_STATUS_SUCCESS - The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_RESAMPLE_DESCRIPTOR                              = 24, /**< Resample config: mode, spatial dims, padding, strides, window dims. @since cuDNN 9.0.0 */
    /**
     * @brief Specifies an operation node for forward resampling with alpha/beta blending.
     *
     *
     * Created with cudnnBackendCreateDescriptor(CUDNN_BACKEND_OPERATION_RESAMPLE_FWD_DESCRIPTOR, &desc);
     * the cuDNN backend resample forward operation descriptor specifies an operation node for
     * forward resampling. It computes the output tensor y of image tensor x resampled according
     * to CUDNN_ATTR_RESAMPLE_MODE, with output scaling alpha and residual add with beta scaling.
     *
     * The resampling mode acts independently on each spatial dimension. For spatial dimension i,
     * the output spatial dimension size y_i can be calculated by combining input image's spatial
     * dimension size x_i, post padding post_i, pre padding pre_i, stride s_i, and window size
     * w_i as: y_i = 1 + (x_i + post_i + pre_i - w_i) / s_i
     *
     * Supported attributes:
     * - CUDNN_ATTR_OPERATION_RESAMPLE_FWD_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_RESAMPLE_DESCRIPTOR)
     *     Resample operation descriptor (CUDNN_BACKEND_RESAMPLE_DESCRIPTOR) instance contains
     *     metadata about the operation.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_RESAMPLE_FWD_XDESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Input tensor descriptor.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_RESAMPLE_FWD_YDESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Output tensor descriptor.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_RESAMPLE_FWD_IDXDESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Tensor containing maxpool or nearest neighbor resampling indices to be used in backprop.
     *     Optional attribute (primarily used for use cases involving training).
     *
     * - CUDNN_ATTR_OPERATION_RESAMPLE_FWD_ALPHA (CUDNN_TYPE_DOUBLE or CUDNN_TYPE_FLOAT, 1 element)
     *     Sets the alpha parameter used in blending.
     *     Optional attribute.
     *     Default value is 1.0.
     *
     * - CUDNN_ATTR_OPERATION_RESAMPLE_FWD_BETA (CUDNN_TYPE_DOUBLE or CUDNN_TYPE_FLOAT, 1 element)
     *     Sets the beta parameter used in blending.
     *     Optional attribute.
     *     Default value is 0.0.
     *
     * Finalization:
     *   In the finalization stage, the attributes are cross checked to make sure there are
     *   no conflicts. The status below may be returned:
     *
     *   CUDNN_STATUS_BAD_PARAM - Invalid or inconsistent attribute values are encountered.
     *     Some examples include:
     *     - The output shape calculated based on the padding and strides does not match the
     *       given output tensor dimensions.
     *     - The shape of the YDESC and IDXDESC (if given) do not match.
     *   CUDNN_STATUS_SUCCESS - The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_OPERATION_RESAMPLE_FWD_DESCRIPTOR                = 25, /**< Forward resampling (pooling/interpolation) with alpha/beta scaling. @since cuDNN 9.0.0 */
    /**
     * @brief Specifies an operation node for backward resampling computing dx from dy.
     *
     *
     * Created with cudnnBackendCreateDescriptor(CUDNN_BACKEND_OPERATION_RESAMPLE_BWD_DESCRIPTOR, &desc);
     * the cuDNN backend resample backward operation descriptor specifies an operation node for
     * backward resampling. It computes the input tensor gradient dx from output tensor gradient
     * dy with backward resampling done according to CUDNN_ATTR_RESAMPLE_MODE with output scaling
     * alpha and residual add with beta scaling.
     *
     * Supported attributes:
     * - CUDNN_ATTR_OPERATION_RESAMPLE_BWD_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_RESAMPLE_DESCRIPTOR)
     *     Resample operation descriptor (CUDNN_BACKEND_RESAMPLE_DESCRIPTOR) instance contains
     *     metadata about the operation.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_RESAMPLE_BWD_DXDESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Input tensor gradient descriptor.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_RESAMPLE_BWD_DYDESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Output tensor gradient descriptor.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_RESAMPLE_BWD_IDXDESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Tensor containing maxpool or nearest neighbor resampling indices to be used in backprop.
     *     Optional attribute.
     *
     * - CUDNN_ATTR_OPERATION_RESAMPLE_BWD_ALPHA (CUDNN_TYPE_DOUBLE or CUDNN_TYPE_FLOAT, 1 element)
     *     Sets the alpha parameter used in blending.
     *     Optional attribute.
     *     Default value is 1.0.
     *
     * - CUDNN_ATTR_OPERATION_RESAMPLE_BWD_BETA (CUDNN_TYPE_DOUBLE or CUDNN_TYPE_FLOAT, 1 element)
     *     Sets the beta parameter used in blending.
     *     Optional attribute.
     *     Default value is 0.0.
     *
     * - CUDNN_ATTR_OPERATION_RESAMPLE_BWD_XDESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Input tensor X descriptor.
     *     Optional attribute.
     *     Required for NCHW layout.
     *
     * - CUDNN_ATTR_OPERATION_RESAMPLE_BWD_YDESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Input tensor Y descriptor.
     *     Optional attribute.
     *     Required for NCHW layout.
     *
     * Finalization:
     *   In the finalization stage, the attributes are cross checked to make sure there are
     *   no conflicts. The status below may be returned:
     *
     *   CUDNN_STATUS_BAD_PARAM - Invalid or inconsistent attribute values are encountered.
     *     Some examples include:
     *     - The output shape calculated based on the padding and strides does not match the
     *       given output tensor dimensions.
     *     - The shape of YDESC and IDXDESC (if given) do not match.
     *   CUDNN_STATUS_SUCCESS - The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_OPERATION_RESAMPLE_BWD_DESCRIPTOR                = 26, /**< Backward resampling: computes dx from dy with alpha/beta scaling. @since cuDNN 9.0.0 */
    /**
     * @brief Specifies an operation node for concatenating tensors along a given axis.
     *
     *
     * Created with cudnnBackendCreateDescriptor(CUDNN_BACKEND_OPERATION_CONCAT_DESCRIPTOR, &desc);
     * the cuDNN backend concatenation operation descriptor specifies an operation node for
     * concatenating a given vector of tensors along a given concatenation axis.
     *
     * This operation also supports an in-place mode, where one of the input tensors is already
     * assumed to be at the correct location in the output tensor, that is, they share the same
     * device buffer.
     *
     * Supported attributes:
     * - CUDNN_ATTR_OPERATION_CONCAT_AXIS (CUDNN_TYPE_INT64)
     *     The dimension which tensors are being concatenated over.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_CONCAT_INPUT_DESCS (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1+ elements of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     A vector of input tensor descriptors, which are concatenated in the same order as
     *     provided in this vector.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_CONCAT_INPLACE_INDEX (CUDNN_TYPE_INT64)
     *     The index of input tensor in the vector of input tensor descriptors that is already
     *     present in-place in the output tensor.
     *     Optional attribute.
     *
     * - CUDNN_ATTR_OPERATION_CONCAT_OUTPUT_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     The output tensor descriptor for the result from concatenation of input tensors.
     *     Required attribute.
     *
     * Finalization:
     *   CUDNN_STATUS_BAD_PARAM - Invalid or inconsistent attribute values are encountered.
     *     Some examples include:
     *     - The tensors involved in the operation should have the same shape in all dimensions
     *       except the dimension that they are being concatenated over.
     *     - The output tensor shape in the concatenating dimension should equal the sum of
     *       tensor shape of all input tensors in that same dimension.
     *     - Concatenation axis should be a valid tensor dimension.
     *     - If provided, the in-place input tensor index should be a valid index in the vector
     *       of input tensor descriptors.
     *   CUDNN_STATUS_SUCCESS - The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_OPERATION_CONCAT_DESCRIPTOR                      = 27, /**< Concatenates multiple tensors along a given axis. @since cuDNN 9.0.0 */
    /**
     * @brief Specifies an operation node for updating or waiting on a flag variable.
     *
     *
     * Created with cudnnBackendCreateDescriptor(CUDNN_BACKEND_OPERATION_SIGNAL_DESCRIPTOR, &desc);
     * the cuDNN backend signal operation descriptor specifies an operation node for updating
     * or waiting on a flag variable. Signaling operations can be used to communicate between
     * cuDNN operation graphs, even with operation graphs in another GPU.
     *
     * This operation, to connect to other nodes in the graph, also has a pass-through input
     * tensor, which is not operated on and is just passed along to the output tensor. This
     * mandatory pass-through input tensor helps in determining the predecessor node after which
     * the signal operation should be executed. The optional output tensor helps in determining
     * the successor node before which the signal execution should have completed. It is also
     * guaranteed that for a non-virtual tensor as the output tensor, all writes for the tensor
     * will have taken place before the signal value is updated by the operation.
     *
     * Supported attributes:
     * - CUDNN_ATTR_OPERATION_SIGNAL_MODE (CUDNN_TYPE_SIGNAL_MODE)
     *     The signaling mode to use.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_SIGNAL_FLAGDESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Flag tensor descriptor.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_SIGNAL_VALUE (CUDNN_TYPE_INT64)
     *     The scalar value to compare or update the flag variable with.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_SIGNAL_XDESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     A pass-through input tensor to enable connecting this signal operation to other nodes
     *     in the graph.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_SIGNAL_YDESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     The output tensor for the pass-through input tensor.
     *     Optional attribute.
     *
     * Finalization:
     *   In the finalization stage, the attributes are cross checked to make sure there are
     *   no conflicts. The status below may be returned:
     *
     *   CUDNN_STATUS_BAD_PARAM - Invalid or inconsistent attribute values are encountered.
     *   CUDNN_STATUS_SUCCESS - The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_OPERATION_SIGNAL_DESCRIPTOR                      = 28, /**< Updates or waits on a flag variable for inter-graph signaling. @since cuDNN 9.0.0 */
    /**
     * @brief Specifies a node for forward normalization producing normalized output Y from input X.
     *
     *
     * Created with cudnnBackendCreateDescriptor(CUDNN_BACKEND_OPERATION_NORM_FORWARD_DESCRIPTOR, &desc);
     * the cuDNN backend normalization forward operation specifies a node for a forward
     * normalization that takes as input a tensor X and produces a normalized output Y with
     * the normalization mode set by the CUDNN_ATTR_OPERATION_NORM_FWD_MODE attribute. The
     * operation supports optional running stats computation and allows for storing the computed
     * means and variances for reuse in the backwards calculation depending on the setting of
     * the CUDNN_ATTR_OPERATION_NORM_FWD_PHASE attribute.
     *
     * Limitations:
     * - Does not support CUDNN_GROUP_NORM mode.
     * - Batch norm only supports forward training and not forward inference.
     *
     * Supported configurations:
     *   CUDNN_NORM_FWD_TRAINING:
     *     CUDNN_LAYER_NORM: Yes, CUDNN_INSTANCE_NORM: Yes, CUDNN_BATCH_NORM: Yes,
     *     CUDNN_GROUP_NORM: No, CUDNN_RMS_NORM: Yes.
     *   CUDNN_NORM_FWD_INFERENCE:
     *     CUDNN_LAYER_NORM: Yes, CUDNN_INSTANCE_NORM: Yes, CUDNN_BATCH_NORM: No,
     *     CUDNN_GROUP_NORM: No, CUDNN_RMS_NORM: Yes.
     *
     * Note: In addition to single-GPU, batch normalization supports running on single node
     * multi-GPUs, while other normalization modes only support running on a single GPU. For
     * more information, refer to the NormAddRelu pattern.
     *
     * Supported attributes:
     * - CUDNN_ATTR_OPERATION_NORM_FWD_MODE (CUDNN_TYPE_NORM_MODE, 1 element)
     *     Chooses the normalization mode for the norm forward operation.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_NORM_FWD_PHASE (CUDNN_TYPE_NORM_FWD_PHASE, 1 element)
     *     Selects the training or inference phase for the norm forward operation.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_NORM_FWD_XDESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Input tensor descriptor.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_NORM_FWD_MEAN_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Estimated mean input tensor descriptor for the inference phase and the computed mean
     *     output tensor descriptor for the training phase.
     *     Optional attribute.
     *
     * - CUDNN_ATTR_OPERATION_NORM_FWD_INV_VARIANCE_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Estimated inverse variance input tensor descriptor for the inference phase and the
     *     computed inverse variance output tensor descriptor for the training phase.
     *     Optional attribute.
     *
     * - CUDNN_ATTR_OPERATION_NORM_FWD_SCALE_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Normalization scale input tensor descriptor.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_NORM_FWD_BIAS_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Normalization bias input tensor descriptor.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_NORM_FWD_EPSILON_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Scalar input tensor descriptor for the epsilon value used in normalization calculation.
     *     Note that the attribute CUDNN_ATTR_TENSOR_IS_BY_VALUE of this descriptor should be
     *     set to true.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_NORM_FWD_EXP_AVG_FACTOR_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Scalar input tensor descriptor for the exponential average factor value used in
     *     running stats computation.
     *     Optional attribute.
     *
     * - CUDNN_ATTR_OPERATION_NORM_FWD_INPUT_RUNNING_MEAN_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Input running mean tensor descriptor for the running stats computation in the
     *     training phase.
     *     Optional attribute.
     *
     * - CUDNN_ATTR_OPERATION_NORM_FWD_INPUT_RUNNING_VAR_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Input running variance tensor descriptor for the running stats computation in the
     *     training phase.
     *     Optional attribute.
     *
     * - CUDNN_ATTR_OPERATION_NORM_FWD_OUTPUT_RUNNING_MEAN_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Output running mean tensor descriptor for the running stats computation in the
     *     training phase.
     *     Optional attribute.
     *
     * - CUDNN_ATTR_OPERATION_NORM_FWD_OUTPUT_RUNNING_VAR_DESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Output running variance tensor descriptor for the running stats computation in the
     *     training phase.
     *     Optional attribute.
     *
     * - CUDNN_ATTR_OPERATION_NORM_FWD_YDESC (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1 element of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Tensor descriptor for the output of the normalization operation.
     *     Required attribute.
     *
     * - CUDNN_ATTR_OPERATION_NORM_FWD_PEER_STAT_DESCS (CUDNN_TYPE_BACKEND_DESCRIPTOR, 1+ elements of CUDNN_BACKEND_TENSOR_DESCRIPTOR)
     *     Vector of tensor descriptors for the communication buffers used in multi-GPU
     *     normalization. Typically, one buffer is provided for every GPU in the node. This
     *     is an optional attribute only used for multi-GPU tensor stats reduction.
     *     Optional attribute.
     *
     * Finalization:
     *   In the finalization stage, the attributes are checked to ensure there are no conflicts.
     *
     *   CUDNN_STATUS_BAD_PARAM - Invalid or inconsistent attribute values are encountered.
     *     Some examples include:
     *     - The output tensor dimensions do not match the input tensor dimensions.
     *     - The channel count C for the mean, scale, bias, and inv_variance tensors do not match.
     *   CUDNN_STATUS_SUCCESS - The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_OPERATION_NORM_FORWARD_DESCRIPTOR                = 29, /**< Forward normalization (layer/instance/batch/RMS) with optional running stats. @since cuDNN 9.0.0 */
    /**
     * @brief the cuDNN backend normalization backward operation specifies a node for a backward normalization
     *
     * that takes as input the gradient tensor dY and outputs the gradient tensor dX and weight gradients
     * dScale and dBias. The normalization mode is set using the CUDNN_ATTR_OPERATION_NORM_BWD_MODE attribute.
     *
     * Limitations:
     *   - Does not support CUDNN_GROUP_NORM mode.
     *
     * Supported Configurations:
     *   CUDNN_LAYER_NORM    : Yes
     *   CUDNN_INSTANCE_NORM : Yes
     *   CUDNN_BATCH_NORM    : Yes
     *   CUDNN_GROUP_NORM    : No
     *   CUDNN_RMS_NORM      : Yes
     *
     * Note: In addition to single GPU, CUDNN_BATCH_NORM also supports single node multi-GPU batch norm,
     * while other normalization modes only support running on a single GPU. For more information, refer
     * to the DReluForkDNorm pattern.
     *
     * Attributes:
     *
     *   CUDNN_ATTR_OPERATION_NORM_BWD_MODE
     *     Chooses the normalization mode for the norm backward operation.
     *     - CUDNN_TYPE_NORM_MODE; one element.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_NORM_BWD_XDESC
     *     Input tensor descriptor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_NORM_BWD_MEAN_DESC
     *     Saved mean input tensor descriptor for reusing the mean computed during the forward computation
     *     of the training phase.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Optional attribute.
     *
     *   CUDNN_ATTR_OPERATION_NORM_BWD_INV_VARIANCE_DESC
     *     Saved inverse variance input tensor descriptor for reusing the mean computed during the forward
     *     computation of the training phase.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Optional attribute.
     *
     *   CUDNN_ATTR_OPERATION_NORM_BWD_DYDESC
     *     Gradient tensor descriptor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_NORM_BWD_SCALE_DESC
     *     Normalization scale descriptor. Note that the bias descriptor is not necessary for the backward pass.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_NORM_BWD_EPSILON_DESC
     *     Scalar input tensor descriptor for the epsilon value. The epsilon values are needed only if the
     *     saved mean and variances are not passed as inputs to the operation. Note that the attribute
     *     CUDNN_ATTR_TENSOR_IS_BY_VALUE of this descriptor should be set to true.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Optional attribute.
     *
     *   CUDNN_ATTR_OPERATION_NORM_BWD_DSCALE_DESC
     *     Scale gradient tensor descriptor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_NORM_BWD_DBIAS_DESC
     *     Bias gradient tensor descriptor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_NORM_BWD_DXDESC
     *     Input gradient tensor descriptor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_NORM_BWD_PEER_STAT_DESCS
     *     Vector of tensor descriptors for the communication buffers used in multi-GPU normalization.
     *     Typically, one buffer is provided for every GPU in the node. This is an optional attribute only
     *     used for multi-GPU tensor stats reduction.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one or more elements of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Optional attribute.
     *
     * Finalization:
     *   CUDNN_STATUS_BAD_PARAM
     *     Invalid or inconsistent attribute values are encountered. Some examples include:
     *       - The tensor dimensions of the gradient tensors dY, dX, and input tensor X, do not match.
     *       - The channel count C for the mean, scale, and inv_variance tensors do not match.
     *   CUDNN_STATUS_SUCCESS
     *     The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_OPERATION_NORM_BACKWARD_DESCRIPTOR               = 30, /**< Backward normalization: computes dX, dScale, dBias from dY. @since cuDNN 9.0.0 */
    /**
     * @brief Reshapes a tensor from one layout to another.
     *
     *
     * Note: No dedicated RST documentation section exists for this descriptor. The information
     * below is derived from the header (cudnn_graph.h) attribute definitions.
     *
     * Attributes:
     *
     *   CUDNN_ATTR_OPERATION_RESHAPE_XDESC
     *     Reshape input tensor descriptor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_RESHAPE_YDESC
     *     Reshape output tensor descriptor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     * Note: Documentation pending.
     * @since cuDNN 9.0.0
     */
    CUDNN_BACKEND_OPERATION_RESHAPE_DESCRIPTOR                     = 31, /**< Reshapes a tensor from one layout to another. @since cuDNN 9.0.0 */
    /**
     * @brief the cuDNN backend Rng descriptor specifies any metadata, including the probability distribution
     *
     * that will be used to generate the tensor and the distribution's corresponding parameters.
     *
     * Attributes:
     *
     *   CUDNN_ATTR_RNG_DISTRIBUTION
     *     The probability distribution used for the rng operation.
     *     - CUDNN_TYPE_RNG_DISTRIBUTION; one element.
     *     - Default value is CUDNN_RNG_DISTRIBUTION_BERNOULLI.
     *
     *   CUDNN_ATTR_RNG_NORMAL_DIST_MEAN
     *     The mean value for the normal distribution, used if
     *     CUDNN_ATTR_RNG_DISTRIBUTION = CUDNN_RNG_DISTRIBUTION_NORMAL.
     *     - CUDNN_TYPE_DOUBLE; one element.
     *     - Default value is -1.
     *
     *   CUDNN_ATTR_RNG_NORMAL_DIST_STANDARD_DEVIATION
     *     The standard deviation value for the normal distribution, used if
     *     CUDNN_ATTR_RNG_DISTRIBUTION = CUDNN_RNG_DISTRIBUTION_NORMAL.
     *     - CUDNN_TYPE_DOUBLE; one element.
     *     - Default value is -1.
     *
     * Finalization:
     *   Return values of cudnnBackendFinalize(desc) where desc is CUDNN_BACKEND_RNG_DESCRIPTOR are:
     *
     *   CUDNN_STATUS_BAD_PARAM
     *     An invalid attribute value was encountered. Some examples include:
     *       - If CUDNN_ATTR_RNG_DISTRIBUTION = CUDNN_RNG_DISTRIBUTION_NORMAL and the standard
     *         deviation supplied is negative.
     *       - If CUDNN_ATTR_RNG_DISTRIBUTION = CUDNN_RNG_DISTRIBUTION_UNIFORM and the maximum
     *         value of the range is lower than minimum value.
     *       - If CUDNN_ATTR_RNG_DISTRIBUTION = CUDNN_RNG_DISTRIBUTION_BERNOULLI and the
     *         probability supplied is negative.
     *   CUDNN_STATUS_SUCCESS
     *     The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_RNG_DESCRIPTOR                                   = 32, /**< RNG config: distribution type (Bernoulli/uniform/normal) and parameters. @since cuDNN 9.0.0 */
    /**
     * @brief the cuDNN backend Rng operation descriptor specifies an operation node for generating a tensor
     *
     * with random numbers based on the probability distribution specified in the Rng descriptor.
     *
     * The random numbers are generated using a Philox random number generator (RNG) as described in
     * Pytorch (https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/core/PhiloxRNGEngine.h).
     * The Philox object takes a seed value, a subsequence for starting the generation, and an offset
     * for the subsequence. Seed and offset can be set by using the attributes. The subsequence is
     * internally set, to ensure independent random numbers.
     *
     * Attributes:
     *
     *   CUDNN_ATTR_OPERATION_RNG_DESC
     *     Rng descriptor (CUDNN_BACKEND_RNG_DESCRIPTOR) instance containing metadata about the operation.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_RNG_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_RNG_YDESC
     *     Output tensor descriptor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_RNG_SEED
     *     Sets the seed for the random number generator which creates the Y tensor. It can be a host
     *     INT64 value or a backend descriptor binded to a value on the device. Only supports a tensor
     *     with all dimensions set to 1 and all strides set to 1.
     *     - CUDNN_TYPE_INT64; one element or CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor
     *       type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Optional attribute.
     *     - Default value is 0.
     *
     *   CUDNN_ATTR_OPERATION_RNG_OFFSET_DESC
     *     Tensor descriptor for the offset used in the RNG Philox object. Only supports a tensor with
     *     all dimensions set to 1 and all strides set to 1.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     * Finalization:
     *   CUDNN_STATUS_BAD_PARAM
     *     CUDNN_ATTR_OPERATION_RNG_OFFSET_DESC or CUDNN_ATTR_OPERATION_RNG_SEED do not have all
     *     dimensions and strides set to 1.
     *   CUDNN_STATUS_SUCCESS
     *     The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_OPERATION_RNG_DESCRIPTOR                         = 33, /**< Generates a tensor of random numbers from a specified distribution. @since cuDNN 9.0.0 */
    /**
     * @brief the cuDNN backend kernel cache helps significantly reduce execution plan finalizing time for
     *
     * use cases that have same-topology dynamic shape operation graph by binding the previously
     * compiled applicable kernel to the execution plan instead of re-compiling a new one from scratch.
     * This is used with execution plans containing a graph with
     * CUDNN_ATTR_OPERATIONGRAPH_IS_DYNAMIC_SHAPE_ENABLED enabled.
     *
     * Attributes:
     *
     *   CUDNN_ATTR_KERNEL_CACHE_IS_ENGINECFG_KERNEL_CACHED
     *     An attribute used to query whether a given engine config is cached.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element.
     *     - Read-only attribute using the cudnnBackendGetAttribute() API. The engine config in question
     *       is to be passed into this attribute as a constant input through arrayOfElements and the
     *       elementCount will serve as the resulting output, a value of zero meaning not cached and a
     *       positive number meaning that it is cached.
     *     - Required attribute.
     *
     * Finalization:
     *   CUDNN_STATUS_SUCCESS
     *     The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_KERNEL_CACHE_DESCRIPTOR                          = 34, /**< Caches compiled kernels to speed up plan finalization for dynamic-shape graphs. @since cuDNN 9.4.0 */
    /**
     * @brief the cuDNN backend paged cache load operation descriptor is used with a fused flash attention fprop
     *
     * graph, and specifies an operation node for reconstructing the k- or v-cache.
     *
     * The k/v-cache is reconstructed by using a page table tensor to look up the location of a specific
     * sequence ID in a non-contiguous container tensor. Storing a k/v-cache non-contiguously enables
     * efficient memory management by avoiding fragmentation. For more information, refer to the
     * Paged Attention paper (https://arxiv.org/abs/2309.06180).
     *
     * Attributes:
     *
     *   CUDNN_ATTR_OPERATION_PAGED_CACHE_LOAD_YDESC
     *     Virtual output tensor descriptor, containing the reconstructed k/v-cache.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Datatype: FP16, BF16, or FP8
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_PAGED_CACHE_LOAD_CONTAINER_DESC
     *     A non-virtual tensor descriptor with dimensions [num_blocks,H,block_size,D] containing the
     *     k/v-cache. The k/v-cache is divided into num_blocks of [H,block_size,D] tensors, where
     *     block_size is a parameter chosen by the user. A smaller block_size leads to less fragmentation,
     *     but also less parallelism. num_blocks is arbitrary and depends on the size of the allocated
     *     k/v-cache.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Datatype: FP16, BF16, or FP8
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_PAGED_CACHE_LOAD_PAGE_TABLE_DESC
     *     A non-virtual tensor descriptor of dimensions [B,1,ceil(max_seq_size/block_size),1] pointing
     *     to the lookup table.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Datatype: INT32
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_PAGED_CACHE_LOAD_SEQUENCE_DESC
     *     A non-virtual [B,1,1,1] tensor descriptor indicates which sequence numbers from the k/v-cache
     *     are requested. For each batch, all items from the container will be copied from sequence 0 to
     *     sequence number 1.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Datatype: INT32 or INT64
     *     - Sequence numbers are in the interval [1, max_seq_size]
     *     - Required attribute.
     *
     * Finalization:
     *   CUDNN_STATUS_BAD_PARAM
     *     Types or dimensions of one or more of the input/output tensors are invalid.
     *   CUDNN_STATUS_SUCCESS
     *     The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_OPERATION_PAGED_CACHE_LOAD_DESCRIPTOR            = 35, /**< Reconstructs K/V-cache pages in fused flash attention forward graphs. @since cuDNN 9.4.0 */
    /**
     * @brief the cuDNN block scale quantize descriptor specifies the parameters for the block scale quantize
     *
     * operation to output block scaled tensors.
     *
     * Attributes:
     *
     *   CUDNN_ATTR_OPERATION_BLOCK_SCALE_QUANTIZE_XDESC
     *     Input float tensor to quantize.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_BLOCK_SCALE_QUANTIZE_YDESC
     *     Quantized output tensor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_BLOCK_SCALE_QUANTIZE_SCALE_DESC
     *     Per-block scaling factors output.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_BLOCK_SCALE_QUANTIZE_MATH_PREC
     *     The math precision of the computation.
     *     - CUDNN_TYPE_DATA_TYPE; one element.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_BLOCK_SCALE_QUANTIZE_BLOCK_SIZE
     *     The number of elements per block to perform block scaling.
     *     - CUDNN_TYPE_INT32; one element.
     *     - Required attribute.
     *
     * Finalization:
     *   CUDNN_STATUS_BAD_PARAM
     *     An invalid attribute value was encountered. Some examples include:
     *       - Tensor shape mismatch between x, y, and scale tensors.
     *       - Data type mismatch between y and scale tensors.
     *   CUDNN_STATUS_SUCCESS
     *     The descriptor was finalized successfully.
     *
     * @since cuDNN 9.7.0
     */
    CUDNN_BACKEND_OPERATION_BLOCK_SCALE_QUANTIZE_DESCRIPTOR        = 36, /**< Block-scale quantization: converts float tensors to block-scaled format. @since cuDNN 9.7.0 */
    /**
     * @brief the cuDNN block scale dequantize descriptor specifies the parameters for the block scale dequantize
     *
     * operation to take in block scaled tensors.
     *
     * Attributes:
     *
     *   CUDNN_ATTR_OPERATION_BLOCK_SCALE_DEQUANTIZE_XDESC
     *     Quantized input tensor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_BLOCK_SCALE_DEQUANTIZE_SCALE_DESC
     *     Per-block scale factors.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_BLOCK_SCALE_DEQUANTIZE_YDESC
     *     Dequantized output tensor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_BLOCK_SCALE_DEQUANTIZE_MATH_PREC
     *     The math precision of the computation.
     *     - CUDNN_TYPE_DATA_TYPE; one element.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_BLOCK_SCALE_DEQUANTIZE_BLOCK_SIZE
     *     The number of elements per block to perform block scaling.
     *     - CUDNN_TYPE_INT32; one element.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_BLOCK_SCALE_DEQUANTIZE_NEG_SCALE
     *     Negative scale handling.
     *     - Optional attribute.
     *
     * Finalization:
     *   CUDNN_STATUS_BAD_PARAM
     *     An invalid attribute value was encountered. Some examples include:
     *       - Tensor shape mismatch between x, scale, and y tensors.
     *       - Data type mismatch between x and scale tensors.
     *   CUDNN_STATUS_SUCCESS
     *     The descriptor was finalized successfully.
     *
     * @since cuDNN 9.7.0
     */
    CUDNN_BACKEND_OPERATION_BLOCK_SCALE_DEQUANTIZE_DESCRIPTOR      = 37, /**< Block-scale dequantization: converts block-scaled tensors back to float. @since cuDNN 9.7.0 */
    /**
     * @brief the cuDNN device property descriptor specifies the properties of a device.
     *
     *
     * Attributes:
     *
     *   CUDNN_ATTR_DEVICEPROP_DEVICE_ID
     *     The CUDA device ID of the device that the descriptor targets.
     *     - CUDNN_TYPE_INT32; one element.
     *     - Optional attribute.
     *
     *   CUDNN_ATTR_DEVICEPROP_HANDLE
     *     The cuDNN handle of the device that the descriptor targets.
     *     - CUDNN_TYPE_HANDLE; one element.
     *     - Optional attribute.
     *
     *   CUDNN_ATTR_DEVICEPROP_JSON_REPRESENTATION
     *     The JSON representation of the device that the descriptor targets.
     *     - CUDNN_TYPE_CHAR; one element.
     *     - Optional attribute.
     *
     * Finalization:
     *   CUDNN_STATUS_BAD_PARAM
     *     An invalid attribute value was encountered, for example, the provided JSON representation
     *     is invalid.
     *   CUDNN_STATUS_NOT_INITIALIZED
     *     For some reason, querying the device properties failed.
     *   CUDNN_STATUS_NOT_SUPPORTED_ARCH_MISMATCH
     *     The target device is not supported.
     *   CUDNN_STATUS_SUCCESS
     *     The descriptor was finalized successfully.
     */
    CUDNN_BACKEND_DEVICEPROP_DESCRIPTOR                            = 38, /**< CUDA device properties: device ID, handle, JSON representation. @since cuDNN 9.8.0 */
    /**
     * @brief Expands a band matrix into a full matrix representation. This operation takes a compact
     *
     * band-storage tensor and expands it along a specified axis into a full dense matrix, filling
     * positions outside the band with a configurable pad value.
     *
     * Note: No dedicated RST documentation section exists for this descriptor. The information
     * below is derived from the header (cudnn_graph.h) attribute definitions.
     *
     * Attributes:
     *
     *   CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_XDESC
     *     Band matrix input tensor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_YDESC
     *     Expanded output tensor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_LOWER_BANDWIDTH
     *     Lower bandwidth of the band.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_UPPER_BANDWIDTH
     *     Upper bandwidth of the band.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_AXIS
     *     Axis along which to expand.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_PAD_VALUE
     *     Padding value outside the band.
     *     - Optional attribute.
     *
     *   CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_KV_TOKEN_OFFSET_DESC
     *     KV token offset descriptor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Optional attribute.
     *
     *   CUDNN_ATTR_OPERATION_EXPAND_BAND_MATRIX_SPECULATIVE_MASK_DESC
     *     Speculative decoding mask.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Optional attribute.
     *
     * Note: Documentation pending.
     * @since cuDNN 9.10.0
     */
    CUDNN_BACKEND_OPERATION_EXPAND_BAND_MATRIX_DESCRIPTOR          = 39, /**< Expands a band matrix into a full matrix representation. @since cuDNN 9.10.0 */
    /**
     * @brief Contracts a full matrix into a band matrix representation. This operation extracts the band
     *
     * portion of a dense matrix along a specified axis into compact band storage, using a configurable
     * pad value for out-of-bounds elements.
     *
     * Note: No dedicated RST documentation section exists for this descriptor. The information
     * below is derived from the header (cudnn_graph.h) attribute definitions.
     *
     * Attributes:
     *
     *   CUDNN_ATTR_OPERATION_CONTRACT_BAND_MATRIX_XDESC
     *     Full matrix input tensor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_CONTRACT_BAND_MATRIX_YDESC
     *     Contracted band output tensor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_CONTRACT_BAND_MATRIX_LOWER_BANDWIDTH
     *     Lower bandwidth.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_CONTRACT_BAND_MATRIX_UPPER_BANDWIDTH
     *     Upper bandwidth.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_CONTRACT_BAND_MATRIX_AXIS
     *     Axis along which to contract.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_CONTRACT_BAND_MATRIX_PAD_VALUE
     *     Padding value.
     *     - Optional attribute.
     *
     *   CUDNN_ATTR_OPERATION_CONTRACT_BAND_MAX_TOKEN_VALUE
     *     Maximum token value for contraction.
     *     - Optional attribute.
     *
     * Note: Documentation pending.
     * @since cuDNN 9.10.0
     */
    CUDNN_BACKEND_OPERATION_CONTRACT_BAND_MATRIX_DESCRIPTOR        = 40, /**< Contracts a full matrix into a band matrix representation. @since cuDNN 9.10.0 */
    /**
     * @brief the cuDNN scaled dot-product attention forward operation descriptor represents a flash attention
     *
     * forward computation as a single operation, obviating in some cases the need to represent flash
     * attention as many separate operations. Only one basic flash attention operation is supported:
     *
     *   O = softmax((Q * K^t) * scale) * V
     *
     * An optional padding mask is also supported. The padding mask does not have its own attribute, but
     * is implicitly enabled if CUDNN_ATTR_OPERATION_SDPA_FWD_SEQ_LEN_QDESC and
     * CUDNN_ATTR_OPERATION_SDPA_FWD_SEQ_LEN_KVDESC are both specified. In this case, the formula becomes:
     *
     *   O = softmax(padding_mask((Q * K^t) * scale)) * V
     *
     * Furthermore, a block mask is also supported. A block mask can be applied to the scores to mask out
     * entire blocks. In this case, the formula becomes:
     *
     *   O = softmax(padding_mask(block_mask(Q * K^t) * scale)) * V
     *
     * For ease of use, access this operation through the frontend's attention API, which can automatically
     * select this operation depending on the features requested and the cuDNN backend version. The details
     * of this backend descriptor type are provided here for completeness.
     *
     * Attributes:
     *
     *   CUDNN_ATTR_OPERATION_SDPA_FWD_QDESC
     *     Input tensor descriptor representing the Q input to the above formula.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Tensor must be of dimensions (B, H_q, S_q, D_qk).
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_SDPA_FWD_KDESC
     *     Input tensor descriptor representing the K input to the above formula, or the K page container
     *     if paged attention for K is enabled (if CUDNN_ATTR_OPERATION_SDPA_FWD_PAGE_TABLE_KDESC is given).
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Tensor must be of dimensions:
     *       - If paged attention is disabled: (B, H_k, S_kv, D_qk). Note that this attribute represents
     *         a literal K tensor and not its transpose K^t.
     *       - If paged attention is enabled: (num_blocks_k, H_k, bs_k, D_qk) (where bs_k is the K
     *         block size).
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_SDPA_FWD_VDESC
     *     Input tensor descriptor representing the V input to the above formula, or the V page container
     *     if paged attention is enabled (if CUDNN_ATTR_OPERATION_SDPA_FWD_PAGE_TABLE_VDESC is given).
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Tensor must be of dimensions:
     *       - If paged attention is disabled: (B, H_v, S_kv, D_v).
     *       - If paged attention is enabled: (num_blocks_v, H_v, bs_v, D_v) (where bs_v is the V
     *         block size).
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_SDPA_FWD_ODESC
     *     Output tensor descriptor representing the O output of the above formula.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Tensor must be of dimensions (B, H_q, S_q, D_v).
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_SDPA_FWD_STATSDESC
     *     Output tensor descriptor representing the stats output of the softmax in the above formula,
     *     used during training.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Tensor must be of dimensions (B, H_q, S_q, 1).
     *     - Optional attribute.
     *
     *   CUDNN_ATTR_OPERATION_SDPA_FWD_SCALEDESC
     *     Input tensor descriptor representing the scale factor in the above formula.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Single element must be of type CUDNN_DATA_FLOAT.
     *     - Tensor must be of dimensions (1, 1, 1, 1).
     *     - Tensor may be a device-side tensor, or a "by value" (host side) tensor.
     *     - Optional attribute. If not given, a scale of 1.0 is used.
     *
     *   CUDNN_ATTR_OPERATION_SDPA_FWD_BLOCK_MASK_DESC
     *     Input tensor descriptor representing the block mask of the scores assuming block size 128x128.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Elements must be of type CUDNN_DATA_INT8.
     *     - Tensor must be of dimensions (B, H_q, ceil(S_q / 128), ceil(S_kv / 128)).
     *     - Optional attribute.
     *
     *   CUDNN_ATTR_OPERATION_SDPA_FWD_PAGE_TABLE_KDESC
     *     Input tensor descriptor representing the page table for K.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Elements must be of type CUDNN_DATA_INT32.
     *     - Tensor must be of dimensions (B, 1, ceil(S_kv / bs_k), 1).
     *     - Optional attribute.
     *       - If not given, paged attention for K is disabled.
     *       - If given, paged attention for K is enabled, and the attribute
     *         CUDNN_ATTR_OPERATION_SDPA_FWD_SEQ_LEN_KVDESC must also be given.
     *
     *   CUDNN_ATTR_OPERATION_SDPA_FWD_PAGE_TABLE_VDESC
     *     Input tensor descriptor representing the page table for V.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Elements must be of type CUDNN_DATA_INT32.
     *     - Tensor must be of dimensions (B, 1, ceil(S_kv / bs_v), 1).
     *     - Optional attribute.
     *       - If not given, paged attention for V is disabled.
     *       - If given, paged attention for V is enabled, and the attribute
     *         CUDNN_ATTR_OPERATION_SDPA_FWD_SEQ_LEN_KVDESC must also be given.
     *
     *   CUDNN_ATTR_OPERATION_SDPA_FWD_SEQ_LEN_QDESC
     *     Input tensor descriptor representing sequence lengths for Q.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Elements must be of type CUDNN_DATA_INT32 or CUDNN_DATA_INT64.
     *     - Tensor must be of dimensions (B, 1, 1, 1).
     *     - Optional attribute.
     *       - If given and CUDNN_ATTR_OPERATION_SDPA_FWD_SEQ_LEN_KVDESC is also given, a padding
     *         mask is applied.
     *
     *   CUDNN_ATTR_OPERATION_SDPA_FWD_SEQ_LEN_KVDESC
     *     Input tensor descriptor representing sequence lengths for K and V. If specified and
     *     CUDNN_ATTR_OPERATION_SDPA_FWD_SEQ_LEN_QDESC is also specified, a padding mask is applied.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Elements must be of type CUDNN_DATA_INT32 or CUDNN_DATA_INT64.
     *     - Tensor must be of dimensions (B, 1, 1, 1).
     *     - Optional attribute, but required if paged attention is enabled.
     *       - If given and CUDNN_ATTR_OPERATION_SDPA_FWD_SEQ_LEN_QDESC is also given, a padding
     *         mask is applied.
     *
     *   CUDNN_ATTR_OPERATION_SDPA_FWD_DESCALE_QDESC
     *   CUDNN_ATTR_OPERATION_SDPA_FWD_DESCALE_KDESC
     *   CUDNN_ATTR_OPERATION_SDPA_FWD_DESCALE_VDESC
     *   CUDNN_ATTR_OPERATION_SDPA_FWD_DESCALE_SDESC
     *     Input tensor descriptors representing the FP8 descale (dequantization) factors for Q, K, V,
     *     and the post-softmax scores S, respectively. Used only for FP8 attention.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - For per-tensor FP8: a (1, 1, 1, 1) tensor of type CUDNN_DATA_FLOAT. All four are required
     *       when Q, K, and V are FP8.
     *     - For MXFP8 (block-scaled FP8): DESCALE_Q/K/V are instead CUDNN_DATA_FP8_E8M0 per-block scale
     *       tensors (one E8M0 factor per 32-element block, in the F8_128x4 reordered layout), and
     *       DESCALE_S is not used. MXFP8 mode is selected when DESCALE_Q is an E8M0 tensor.
     *
     *   CUDNN_ATTR_OPERATION_SDPA_FWD_SCALE_SDESC
     *   CUDNN_ATTR_OPERATION_SDPA_FWD_SCALE_ODESC
     *     Input tensor descriptors representing the FP8 scale (quantization) factors for the
     *     post-softmax scores S and the output O, respectively. Used only for per-tensor FP8 attention.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Single element must be of type CUDNN_DATA_FLOAT.
     *     - Tensor must be of dimensions (1, 1, 1, 1).
     *     - Optional attribute. Required when Q, K, and V are FP8. Not used for MXFP8 (block-scaled FP8).
     *
     *   CUDNN_ATTR_OPERATION_SDPA_FWD_AMAX_SDESC
     *   CUDNN_ATTR_OPERATION_SDPA_FWD_AMAX_ODESC
     *     Output tensor descriptors representing the FP8 absolute maximum of the post-softmax scores S
     *     and the output O, respectively. Used only for FP8 attention.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Single element must be of type CUDNN_DATA_FLOAT.
     *     - Tensor must be of dimensions (1, 1, 1, 1).
     *     - Optional attribute. AMAX_ODESC is required when Q, K, and V are FP8; AMAX_SDESC is optional.
     *       For MXFP8 (block-scaled FP8) only AMAX_ODESC applies (optional); AMAX_SDESC is not used.
     *
     * Finalization:
     *   CUDNN_STATUS_BAD_PARAM
     *     An invalid attribute value was encountered, for example, the provided input tensor sizes
     *     don't match along the necessary dimensions to make the scaled dot-product attention
     *     operation mathematically possible.
     *   CUDNN_STATUS_SUCCESS
     *     The descriptor was finalized successfully.
     *
     * @since cuDNN 9.13.0
     */
    CUDNN_BACKEND_OPERATION_SDPA_FWD_DESCRIPTOR                    = 41, /**< Scaled dot-product attention forward (fused flash attention). @since cuDNN 9.13.0 */
    /**
     * @brief Mixture-of-Experts grouped matmul with token routing. Performs a grouped matrix multiplication
     *
     * where tokens are routed to different expert weight matrices based on routing indices. Supports
     * gather and scatter modes for efficient expert-parallel computation.
     *
     * Note: No dedicated RST documentation section exists for this descriptor. The information
     * below is derived from the header (cudnn_graph.h) attribute definitions.
     *
     * Attributes:
     *
     *   CUDNN_ATTR_OPERATION_MOE_GROUPED_MATMUL_MODE
     *     Gather/scatter mode for MoE.
     *     - CUDNN_TYPE_MOE_GROUPED_MATMUL_MODE; one element.
     *     - Values: CUDNN_MOE_GROUPED_MATMUL_MODE_NONE (0),
     *               CUDNN_MOE_GROUPED_MATMUL_MODE_GATHER (1),
     *               CUDNN_MOE_GROUPED_MATMUL_MODE_SCATTER (2).
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_MOE_GROUPED_MATMUL_MATH_PREC
     *     Computation precision.
     *     - CUDNN_TYPE_DATA_TYPE; one element.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_MOE_GROUPED_MATMUL_TOKEN_DESC
     *     Token tensor descriptor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_MOE_GROUPED_MATMUL_WEIGHT_DESC
     *     Expert weight tensor descriptor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_MOE_GROUPED_MATMUL_FIRST_TOKEN_OFFSET_DESC
     *     First token offset per expert.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_MOE_GROUPED_MATMUL_OUTPUT_DESC
     *     Output tensor descriptor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_MOE_GROUPED_MATMUL_TOKEN_INDEX_DESC
     *     Token routing index descriptor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Optional attribute (used with gather/scatter modes).
     *
     *   CUDNN_ATTR_OPERATION_MOE_GROUPED_MATMUL_TOKEN_KS_DESC
     *     Token routing weights descriptor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Optional attribute.
     *
     *   CUDNN_ATTR_OPERATION_MOE_GROUPED_MATMUL_TOP_K
     *     Top-K experts per token.
     *     - Optional attribute.
     *
     * Note: Documentation pending.
     * @since cuDNN 9.15.0
     */
    CUDNN_BACKEND_OPERATION_MOE_GROUPED_MATMUL_DESCRIPTOR          = 42, /**< Mixture-of-Experts grouped matmul with token routing. @since cuDNN 9.15.0 */
    /**
     * @brief Scaled dot-product attention backward operation. Computes gradients dQ, dK, and dV for the
     *
     * backward pass of flash attention given the forward pass outputs and the incoming gradient dO.
     *
     * Note: No dedicated RST documentation section exists for this descriptor. The information
     * below is derived from the header (cudnn_graph.h) attribute definitions.
     *
     * Attributes:
     *
     *   CUDNN_ATTR_OPERATION_SDPA_BWD_QDESC
     *     Query tensor descriptor (forward input).
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_SDPA_BWD_KDESC
     *     Key tensor descriptor (forward input).
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_SDPA_BWD_VDESC
     *     Value tensor descriptor (forward input).
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_SDPA_BWD_ODESC
     *     Forward output tensor descriptor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_SDPA_BWD_STATSDESC
     *     Forward statistics descriptor (softmax stats from the forward pass).
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_SDPA_BWD_SCALEDESC
     *     Attention scaling factor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Optional attribute.
     *
     *   CUDNN_ATTR_OPERATION_SDPA_BWD_SEQ_LEN_QDESC
     *     Query sequence length tensor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Optional attribute.
     *
     *   CUDNN_ATTR_OPERATION_SDPA_BWD_SEQ_LEN_KVDESC
     *     Key-value sequence length tensor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Optional attribute.
     *
     *   CUDNN_ATTR_OPERATION_SDPA_BWD_DQDESC
     *     Query gradient output tensor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_SDPA_BWD_DKDESC
     *     Key gradient output tensor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_SDPA_BWD_DVDESC
     *     Value gradient output tensor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_SDPA_BWD_DODDESC
     *     Output gradient input tensor (dO).
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     * Note: Documentation pending.
     * @since cuDNN 9.17.0
     */
    CUDNN_BACKEND_OPERATION_SDPA_BWD_DESCRIPTOR                    = 43, /**< Scaled dot-product attention backward: computes dQ, dK, dV. @since cuDNN 9.17.0 */
    /**
     * @brief Generates a diagonal band attention mask. Produces a mask tensor based on diagonal band
     *
     * boundaries (left and right bounds) relative to the query and key-value sequence positions,
     * commonly used to implement sliding window or local attention patterns.
     *
     * Note: No dedicated RST documentation section exists for this descriptor. The information
     * below is derived from the header (cudnn_graph.h) attribute definitions.
     *
     * Attributes:
     *
     *   CUDNN_ATTR_OPERATION_DIAGONAL_BAND_MASK_XDESC
     *     Input tensor descriptor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_DIAGONAL_BAND_MASK_SEQ_LEN_KVDESC
     *     KV sequence length tensor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Optional attribute.
     *
     *   CUDNN_ATTR_OPERATION_DIAGONAL_BAND_MASK_SEQ_LEN_QDESC
     *     Query sequence length tensor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Optional attribute.
     *
     *   CUDNN_ATTR_OPERATION_DIAGONAL_BAND_MASK_LEFT_BOUND_DESC
     *     Left bound offset descriptor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_DIAGONAL_BAND_MASK_SHIFT_RIGHT_BOUND_DESC
     *     Right bound shift descriptor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_DIAGONAL_BAND_MASK_BDESC
     *     Band descriptor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Optional attribute.
     *
     *   CUDNN_ATTR_OPERATION_DIAGONAL_BAND_MASK_YDESC
     *     Output mask tensor descriptor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_DIAGONAL_BAND_MASK_COMPARISON_MODE
     *     Mask comparison mode.
     *     - Required attribute.
     *
     * Note: Documentation pending.
     * @since cuDNN 9.20.0
     */
    CUDNN_BACKEND_OPERATION_DIAGONAL_BAND_MASK_DESCRIPTOR          = 44, /**< Generates a diagonal band attention mask. @since cuDNN 9.20.0 */
    /**
     * @brief Softmax operation with optional statistics output. Computes the softmax function over
     *
     * the input tensor and optionally outputs intermediate statistics (row-wise max, sum of
     * exponentials) for use in fused attention patterns.
     *
     * Note: No dedicated RST documentation section exists for this descriptor. The information
     * below is derived from the header (cudnn_graph.h) attribute definitions.
     *
     * Attributes:
     *
     *   CUDNN_ATTR_OPERATION_SOFTMAX_XDESC
     *     Softmax input tensor descriptor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_SOFTMAX_YDESC
     *     Softmax output tensor descriptor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Required attribute.
     *
     *   CUDNN_ATTR_OPERATION_SOFTMAX_STATS_DESC
     *     Softmax statistics output.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Optional attribute.
     *
     *   CUDNN_ATTR_OPERATION_SOFTMAX_MAX_DESC
     *     Row-wise max values descriptor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Optional attribute.
     *
     *   CUDNN_ATTR_OPERATION_SOFTMAX_SUM_EXP_DESC
     *     Row-wise sum of exponentials.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Optional attribute.
     *
     *   CUDNN_ATTR_OPERATION_SOFTMAX_SINK_DESC
     *     Softmax sink descriptor.
     *     - CUDNN_TYPE_BACKEND_DESCRIPTOR; one element of descriptor type CUDNN_BACKEND_TENSOR_DESCRIPTOR.
     *     - Optional attribute.
     *
     * Note: Documentation pending.
     * @since cuDNN 9.20.0
     */
    CUDNN_BACKEND_OPERATION_SOFTMAX_DESCRIPTOR                     = 45, /**< Softmax operation with optional statistics output. @since cuDNN 9.20.0 */
    CUDNN_BACKEND_OPERATION_TRANSPOSE_DESCRIPTOR                   = 46, /**< Transpose operation. @since cuDNN 9.22.0 */
    CUDNN_BACKEND_OPERATION_SLICE_DESCRIPTOR                       = 47, /**< Slice operation. @since cuDNN 9.22.0 */
    CUDNN_BACKEND_OPERATION_MOE_GROUPED_MATMUL_BWD_DESCRIPTOR      = 48, /**< Backward MoE grouped matmul. @since cuDNN 9.22.0 */
    CUDNN_BACKEND_OPERATION_ROPE_FWD_DESCRIPTOR                   = 49, /**< Rotary Position Embedding (RoPE) forward. @since cuDNN 9.24.0 */
    CUDNN_BACKEND_OPERATION_ROPE_BWD_DESCRIPTOR                   = 50, /**< Rotary Position Embedding (RoPE) backward. @since cuDNN 9.25.0 */
} cudnnBackendDescriptorType_t;

/** @brief Numerical behavior notes describing properties of engine implementations.
 *  @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_NUMERICAL_NOTE_TENSOR_CORE                 = 0, /**< Engine uses Tensor Core hardware acceleration. @since cuDNN 9.0.0 */
    CUDNN_NUMERICAL_NOTE_DOWN_CONVERT_INPUTS         = 1, /**< Engine down-converts inputs to lower precision for compute. @since cuDNN 9.0.0 */
    CUDNN_NUMERICAL_NOTE_REDUCED_PRECISION_REDUCTION = 2, /**< Engine uses reduced-precision accumulation in reductions. @since cuDNN 9.0.0 */
    CUDNN_NUMERICAL_NOTE_FFT                         = 3, /**< Engine uses FFT-based computation. @since cuDNN 9.0.0 */
    CUDNN_NUMERICAL_NOTE_NONDETERMINISTIC            = 4, /**< Engine may produce non-deterministic results across runs. @since cuDNN 9.0.0 */
    CUDNN_NUMERICAL_NOTE_WINOGRAD                    = 5, /**< Engine uses Winograd transform. @since cuDNN 9.0.0 */
    CUDNN_NUMERICAL_NOTE_WINOGRAD_TILE_4x4           = 6, /**< Engine uses Winograd with 4x4 output tiles. @since cuDNN 9.0.0 */
    CUDNN_NUMERICAL_NOTE_WINOGRAD_TILE_6x6           = 7, /**< Engine uses Winograd with 6x6 output tiles. @since cuDNN 9.0.0 */
    CUDNN_NUMERICAL_NOTE_WINOGRAD_TILE_13x13         = 8, /**< Engine uses Winograd with 13x13 output tiles. @since cuDNN 9.0.0 */
    CUDNN_NUMERICAL_NOTE_STRICT_NAN_PROP             = 9, /**< Engine strictly propagates NaN values. @since cuDNN 9.1.0 */
    CUDNN_NUMERICAL_NOTE_TYPE_COUNT                  = 10, /**< Number of numerical note types. @since cuDNN 9.0.0 */
} cudnnBackendNumericalNote_t;

/** @brief Engine behavior notes describing runtime requirements and capabilities.
 *  @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_BEHAVIOR_NOTE_RUNTIME_COMPILATION             = 0, /**< Engine requires runtime compilation (NVRTC). @since cuDNN 9.0.0 */
    CUDNN_BEHAVIOR_NOTE_REQUIRES_FILTER_INT8x32_REORDER = 1, /**< Engine requires INT8x32-reordered filter tensors. @since cuDNN 9.0.0 */
    CUDNN_BEHAVIOR_NOTE_REQUIRES_BIAS_INT8x32_REORDER   = 2, /**< Engine requires INT8x32-reordered bias tensors. @since cuDNN 9.0.0 */
    CUDNN_BEHAVIOR_NOTE_SUPPORTS_CUDA_GRAPH_NATIVE_API  = 3, /**< Engine supports native CUDA graph capture. @since cuDNN 9.5.0 */
    CUDNN_BEHAVIOR_NOTE_CUBLASLT_DEPENDENCY             = 4, /**< Engine depends on cuBLASLt library. @since cuDNN 9.15.0 */
    CUDNN_BEHAVIOR_NOTE_TYPE_COUNT                      = 5, /**< Number of behavior note types. @since cuDNN 9.0.0 */
} cudnnBackendBehaviorNote_t;

/** @brief Engine tuning knob types for fine-grained engine configuration.
 *  @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_KNOB_TYPE_SPLIT_K CUDNN_DEPRECATED_ENUM          = 0,  /**< @deprecated Split-K factor. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_SWIZZLE                                = 1, /**< Memory access swizzle pattern for coalescing. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_TILE_SIZE                              = 2, /**< Thread block tile size for computation. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_USE_TEX CUDNN_DEPRECATED_ENUM          = 3,  /**< @deprecated Use texture memory. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_EDGE                                   = 4, /**< Edge/boundary handling mode. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_KBLOCK CUDNN_DEPRECATED_ENUM           = 5,  /**< @deprecated K-block size. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_LDGA CUDNN_DEPRECATED_ENUM             = 6,  /**< @deprecated Load granularity A. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_LDGB CUDNN_DEPRECATED_ENUM             = 7,  /**< @deprecated Load granularity B. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_CHUNK_K CUDNN_DEPRECATED_ENUM          = 8,  /**< @deprecated Chunk-K size. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_SPLIT_H CUDNN_DEPRECATED_ENUM          = 9,  /**< @deprecated Split-H factor. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_WINO_TILE CUDNN_DEPRECATED_ENUM        = 10, /**< @deprecated Winograd tile size. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_MULTIPLY                               = 11, /**< Multiplication factor for tiling. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_SPLIT_K_BUF                            = 12, /**< Split-K with separate output buffers. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_TILEK                                  = 13, /**< Tile size along K (contraction) dimension. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_STAGES                                 = 14, /**< Number of pipeline stages. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_REDUCTION_MODE                         = 15, /**< Cross-thread/cross-block reduction strategy. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_CTA_SPLIT_K_MODE CUDNN_DEPRECATED_ENUM = 16, /**< @deprecated CTA split-K mode. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_SPLIT_K_SLC                            = 17, /**< Split-K slice count. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_IDX_MODE                               = 18, /**< Index computation mode. @since cuDNN 9.7.0 */
    CUDNN_KNOB_TYPE_SLICED CUDNN_DEPRECATED_ENUM           = 19, /**< @deprecated Sliced mode. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_SPLIT_RS CUDNN_DEPRECATED_ENUM         = 20, /**< @deprecated Split-RS factor. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_SINGLEBUFFER CUDNN_DEPRECATED_ENUM     = 21, /**< @deprecated Single buffer mode. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_LDGC CUDNN_DEPRECATED_ENUM             = 22, /**< @deprecated Load granularity C. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_SPECFILT                               = 23, /**< Specialized filter processing mode. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_KERNEL_CFG                             = 24, /**< Kernel configuration selector. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_WORKSPACE                              = 25, /**< Workspace size preference. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_TILE_CGA CUDNN_DEPRECATED_ENUM         = 26, /**< @deprecated CGA tile size. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_TILE_CGA_M                             = 27, /**< CGA cluster tile size along M dimension. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_TILE_CGA_N                             = 28, /**< CGA cluster tile size along N dimension. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_BLOCK_SIZE                             = 29, /**< Thread block size (threads per block). @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_OCCUPANCY                              = 30, /**< Target occupancy level. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_ARRAY_SIZE_PER_THREAD                  = 31, /**< Register array size per thread. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_NUM_C_PER_BLOCK CUDNN_DEPRECATED_ENUM  = 32, /**< @deprecated Channels per block. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_SPLIT_COLS                             = 33, /**< Column split factor for parallelism. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_TILE_ROWS                              = 34, /**< Tile size along row dimension. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_TILE_COLS                              = 35, /**< Tile size along column dimension. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_LOAD_SIZE                              = 36, /**< Memory load granularity. @since cuDNN 9.0.0 */
    CUDNN_KNOB_TYPE_CTA_COUNT                              = 37, /**< Number of CTAs (thread blocks) to launch. @since cuDNN 9.7.0 */
    CUDNN_KNOB_TYPE_STREAM_K                               = 38, /**< Stream-K work distribution mode. @since cuDNN 9.7.0 */
    CUDNN_KNOB_TYPE_SPLIT_P_SLC                            = 39, /**< Split-P slice count. @since cuDNN 9.7.0 */
    CUDNN_KNOB_TYPE_TILE_M                                 = 40, /**< Tile size along M (output rows) dimension. @since cuDNN 9.7.0 */
    CUDNN_KNOB_TYPE_TILE_N                                 = 41, /**< Tile size along N (output cols) dimension. @since cuDNN 9.7.0 */
    CUDNN_KNOB_TYPE_WARP_SPEC_CFG                          = 42, /**< Warp specialization configuration. @since cuDNN 9.7.0 */
    CUDNN_KNOB_TYPE_SWAP_AB                                = 43, /**< Swap A and B operands in matmul. @since cuDNN 9.18.0 */
    CUDNN_KNOB_TYPE_INPUT_TMA_ENABLE                       = 44, /**< Enable TMA for input tensors. @since cuDNN 9.22.0 */
    CUDNN_KNOB_TYPE_OUTPUT_TMA_ENABLE                      = 45, /**< Enable TMA for output tensors. @since cuDNN 9.22.0 */
    CUDNN_KNOB_TYPE_COUNTS                                 = 46, /**< Number of knob types. @since cuDNN 9.0.0 */
} cudnnBackendKnobType_t;

/** @brief Preferred tensor layout types reported by engines.
 *  @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_LAYOUT_TYPE_PREFERRED_NCHW   = 0, /**< Prefers NCHW layout. @since cuDNN 9.0.0 */
    CUDNN_LAYOUT_TYPE_PREFERRED_NHWC   = 1, /**< Prefers NHWC layout. @since cuDNN 9.0.0 */
    CUDNN_LAYOUT_TYPE_PREFERRED_PAD4CK = 2, /**< Prefers padded 4CK layout. @since cuDNN 9.0.0 */
    CUDNN_LAYOUT_TYPE_PREFERRED_PAD8CK = 3, /**< Prefers padded 8CK layout. @since cuDNN 9.0.0 */
    CUDNN_LAYOUT_TYPE_COUNT            = 4, /**< Number of layout types. @since cuDNN 9.0.0 */
} cudnnBackendLayoutType_t;

/** @brief Heuristic modes for engine selection.
 *
 *  INSTANT provides fast heuristic lookup, B uses neural-net-based heuristics,
 *  FALLBACK provides functional (non-optimized) results, A is an alias for INSTANT.
 *  @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_HEUR_MODE_INSTANT  = 0, /**< Fast decision-tree heuristic with minimal CPU overhead. @since cuDNN 9.0.0 */
    CUDNN_HEUR_MODE_B        = 1, /**< Neural-net heuristic; 10-100x CPU cost vs INSTANT, better GPU perf. No 3D/grouped/dilated conv. @since cuDNN 9.0.0 */
    CUDNN_HEUR_MODE_FALLBACK = 2, /**< Functional fallback engines with no GPU performance guarantee. @since cuDNN 9.0.0 */
    CUDNN_HEUR_MODE_A        = 3, /**< Decision-tree heuristic (preferred over INSTANT). @since cuDNN 9.0.0 */
    CUDNN_HEUR_MODES_COUNT   = 4, /**< Number of heuristic modes. @since cuDNN 9.0.0 */
} cudnnBackendHeurMode_t;

/** @brief Tensor reordering modes for specialized memory layouts.
 *  @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_TENSOR_REORDERING_NONE     = 0, /**< No tensor reordering applied. @since cuDNN 9.0.0 */
    CUDNN_TENSOR_REORDERING_INT8x32  = 1, /**< INT8 data reordered into 32-element vectors. @since cuDNN 9.0.0 */
    CUDNN_TENSOR_REORDERING_F16x16   = 2, /**< FP16 data reordered into 16-element vectors. @since cuDNN 9.0.0 */
    CUDNN_TENSOR_REORDERING_F8_128x4 = 3, /**< FP8 data reordered into 128x4 blocks. @since cuDNN 9.7.0 */
} cudnnBackendTensorReordering_t;

/** @brief Padding modes for convolution and pooling operations.
 *  @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_ZERO_PAD     = 0, /**< Pads with zeros. @since cuDNN 9.0.0 */
    CUDNN_NEG_INF_PAD  = 1, /**< Pads with negative infinity (for max pooling). @since cuDNN 9.0.0 */
    CUDNN_EDGE_VAL_PAD = 2, /**< Pads by replicating edge values. @since cuDNN 9.0.0 */
} cudnnPaddingMode_t;

/** @brief Normalization modes supported by norm forward/backward operations.
 *  @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_LAYER_NORM     = 0, /**< Layer normalization: normalizes over feature dims per sample. @since cuDNN 9.0.0 */
    CUDNN_INSTANCE_NORM  = 1, /**< Instance normalization: normalizes per instance per channel. @since cuDNN 9.0.0 */
    CUDNN_BATCH_NORM     = 2, /**< Batch normalization: normalizes across the batch dimension. @since cuDNN 9.0.0 */
    CUDNN_GROUP_NORM     = 3, /**< Group normalization (unsupported; returns CUDNN_STATUS_INTERNAL_ERROR). @since cuDNN 9.0.0 */
    CUDNN_RMS_NORM       = 4, /**< Root mean square normalization: normalizes by RMS of activations. @since cuDNN 9.0.0 */
    CUDNN_ADA_LAYER_NORM = 5, /**< Adaptive layer normalization with learned affine parameters. @since cuDNN 9.7.0 */
} cudnnBackendNormMode_t;

/** @brief Normalization forward phase (inference or training).
 *  @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_NORM_FWD_INFERENCE = 0, /**< Inference phase: uses pre-computed running statistics. @since cuDNN 9.0.0 */
    CUDNN_NORM_FWD_TRAINING  = 1, /**< Training phase: computes batch statistics and updates running stats. @since cuDNN 9.0.0 */
} cudnnBackendNormFwdPhase_t;

/** @brief Reshape operation mode.
 *  @since cuDNN 9.22.0
 */
typedef enum {
    CUDNN_RESHAPE_VIEW_ONLY = 0, /**< View-only reshape (no data movement). @since cuDNN 9.22.0 */
    CUDNN_RESHAPE_LOGICAL   = 1, /**< Logical reshape (may involve data movement). @since cuDNN 9.22.0 */
} cudnnBackendReshapeMode_t;

/** @brief Mode of an operation graph.
 *
 *  Optional. The default value (::CUDNN_OPERATIONGRAPH_MODE_AUTO) makes finalize
 *  detect the mode automatically; this is the recommended setting and the only
 *  safe choice for users constructing graphs by hand.
 *
 *  Setting an explicit mode via ::CUDNN_ATTR_OPERATIONGRAPH_MODE is an advanced
 *  hint primarily intended for cudnn_frontend, which knows the canonical shape of
 *  the graph it just built. Setting a wrong mode silently bypasses pattern
 *  matching and causes failures during plan creation or execution. If you are
 *  not going through cudnn_frontend, leave this on AUTO.
 *
 *  @since cuDNN 9.NEXT
 */
typedef enum {
    CUDNN_OPERATIONGRAPH_MODE_AUTO                     = -1, /**< Default; backend detects the mode. */
    CUDNN_OPERATIONGRAPH_MODE_CONV_FORWARD             = 0,
    CUDNN_OPERATIONGRAPH_MODE_CONV_BWD_DATA            = 1,
    CUDNN_OPERATIONGRAPH_MODE_CONV_BWD_FILTER          = 2,
    CUDNN_OPERATIONGRAPH_MODE_NORM_FWD_INFER           = 17,
    CUDNN_OPERATIONGRAPH_MODE_NORM_FWD_TRAIN           = 18,
    CUDNN_OPERATIONGRAPH_MODE_NORM_BWD                 = 19,
    CUDNN_OPERATIONGRAPH_MODE_GENERIC_CONV_FUSION      = 20,
    CUDNN_OPERATIONGRAPH_MODE_RESAMPLE_FWD             = 22,
    CUDNN_OPERATIONGRAPH_MODE_RESAMPLE_BWD             = 23,
    CUDNN_OPERATIONGRAPH_MODE_BLAS_MATMUL              = 25,
    CUDNN_OPERATIONGRAPH_MODE_GENERIC_MATMUL_FUSION    = 26,
    CUDNN_OPERATIONGRAPH_MODE_GENERIC_POINTWISE_FUSION = 27,
    CUDNN_OPERATIONGRAPH_MODE_SDPA_FWD                 = 28, /**< Composite SDPA fwd (matmul + softmax + matmul). */
    CUDNN_OPERATIONGRAPH_MODE_SDPA_BWD                 = 29,
    CUDNN_OPERATIONGRAPH_MODE_UNIFIED_SDPA_FWD         = 30, /**< Unified SDPA fwd (single SdpaForwardOperation). */
    CUDNN_OPERATIONGRAPH_MODE_UNIFIED_SDPA_BWD         = 31,
} cudnnBackendOperationGraphMode_t;

/** @brief Allocates memory for a backend descriptor of the specified type.
 *  @param[in]  descriptorType  The type of descriptor to create.
 *  @param[out] descriptor      Pointer to receive the newly created descriptor.
 *  @retval CUDNN_STATUS_SUCCESS
 *  @retval CUDNN_STATUS_NOT_SUPPORTED
 *  @retval CUDNN_STATUS_ALLOC_FAILED
 *  @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnBackendCreateDescriptor(cudnnBackendDescriptorType_t descriptorType, cudnnBackendDescriptor_t *descriptor);

/** @brief Deallocates a backend descriptor and frees associated memory.
 *  @param[in] descriptor  The descriptor to destroy.
 *  @retval CUDNN_STATUS_SUCCESS
 *  @retval CUDNN_STATUS_ALLOC_FAILED
 *  @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnBackendDestroyDescriptor(cudnnBackendDescriptor_t descriptor);

/** @brief Repurposes pre-allocated memory for a backend descriptor.
 *  @deprecated Since cuDNN 9.2. Use cudnnBackendCreateDescriptor instead.
 *  @param[in] descriptor  The descriptor to initialize.
 *  @return cuDNN status code.
 *  @since cuDNN 9.0.0
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnBackendInitialize(cudnnBackendDescriptor_t descriptor);

/** @brief Validates and finalizes a descriptor. After finalization, attributes become read-only.
 *  @param[in] descriptor  The descriptor to finalize.
 *  @retval CUDNN_STATUS_SUCCESS
 *  @retval CUDNN_STATUS_BAD_PARAM
 *  @retval CUDNN_STATUS_NOT_SUPPORTED
 *  @retval CUDNN_STATUS_INTERNAL_ERROR
 *  @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnBackendFinalize(cudnnBackendDescriptor_t descriptor);

/** @brief Sets an attribute on an unfinalized backend descriptor.
 *  @param[in] descriptor       The target descriptor (must not be finalized).
 *  @param[in] attributeName    The attribute to set.
 *  @param[in] attributeType    The data type of the attribute values.
 *  @param[in] elementCount     Number of elements in @p arrayOfElements.
 *  @param[in] arrayOfElements  Pointer to the attribute value(s).
 *  @retval CUDNN_STATUS_SUCCESS
 *  @retval CUDNN_STATUS_NOT_INITIALIZED
 *  @retval CUDNN_STATUS_BAD_PARAM
 *  @retval CUDNN_STATUS_NOT_SUPPORTED
 *  @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnBackendSetAttribute(cudnnBackendDescriptor_t descriptor,
                         cudnnBackendAttributeName_t attributeName,
                         cudnnBackendAttributeType_t attributeType,
                         int64_t elementCount,
                         const void *arrayOfElements);

/** @brief Retrieves an attribute from a finalized backend descriptor.
 *  @param[in]  descriptor             The source descriptor (must be finalized).
 *  @param[in]  attributeName          The attribute to query.
 *  @param[in]  attributeType          The expected data type of the attribute.
 *  @param[in]  requestedElementCount  Maximum number of elements to retrieve.
 *  @param[out] elementCount           Pointer to receive the actual element count.
 *  @param[out] arrayOfElements        Buffer to receive the attribute value(s).
 *  @retval CUDNN_STATUS_SUCCESS
 *  @retval CUDNN_STATUS_BAD_PARAM
 *  @retval CUDNN_STATUS_NOT_INITIALIZED
 *  @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnBackendGetAttribute(cudnnBackendDescriptor_t const descriptor,
                         cudnnBackendAttributeName_t attributeName,
                         cudnnBackendAttributeType_t attributeType,
                         int64_t requestedElementCount,
                         int64_t *elementCount,
                         void *arrayOfElements);

/** @brief Queries runtime workspace size for an execution plan using a variant pack.
 *  @param[in]  handle                cuDNN handle.
 *  @param[in]  executionPlan         Finalized execution plan descriptor.
 *  @param[in]  variantPack           Finalized variant pack descriptor.
 *  @param[out] workspaceSizeInBytes  Pointer to receive the required workspace size.
 *  @since cuDNN 9.23.0
 */
 cudnnStatus_t CUDNNWINAPI
 cudnnGetExecutionPlanWorkspaceSize(cudnnHandle_t handle,
                                    cudnnBackendDescriptor_t executionPlan,
                                    cudnnBackendDescriptor_t variantPack,
                                    size_t *workspaceSizeInBytes);

/** @brief Runs an execution plan with the given variant pack containing data pointers.
 *  @param[in] handle         cuDNN handle.
 *  @param[in] executionPlan  Finalized execution plan descriptor.
 *  @param[in] variantPack    Finalized variant pack descriptor with data pointers.
 *  @retval CUDNN_STATUS_SUCCESS
 *  @retval CUDNN_STATUS_BAD_PARAM
 *  @retval CUDNN_STATUS_INTERNAL_ERROR
 *  @retval CUDNN_STATUS_EXECUTION_FAILED
 *  @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnBackendExecute(cudnnHandle_t handle, cudnnBackendDescriptor_t executionPlan, cudnnBackendDescriptor_t variantPack);

/** @brief Populates a CUDA graph with nodes from an execution plan.
 *  @param[in]    handle         cuDNN handle.
 *  @param[in]    executionPlan  Finalized execution plan descriptor.
 *  @param[in]    variantPack    Finalized variant pack descriptor.
 *  @param[inout] graph          CUDA graph to populate.
 *  @retval CUDNN_STATUS_SUCCESS
 *  @retval CUDNN_STATUS_BAD_PARAM
 *  @retval CUDNN_STATUS_INTERNAL_ERROR
 *  @retval CUDNN_STATUS_EXECUTION_FAILED
 *  @retval CUDNN_STATUS_NOT_SUPPORTED
 *  @since cuDNN 9.5.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnBackendPopulateCudaGraph(cudnnHandle_t handle,
                              cudnnBackendDescriptor_t executionPlan,
                              cudnnBackendDescriptor_t variantPack,
                              cudaGraph_t graph);

/** @brief Updates an existing CUDA graph with new data pointers from a variant pack.
 *  @param[in]    handle         cuDNN handle.
 *  @param[in]    executionPlan  Finalized execution plan descriptor.
 *  @param[in]    variantPack    Finalized variant pack with updated data pointers.
 *  @param[inout] graph          CUDA graph to update.
 *  @retval CUDNN_STATUS_SUCCESS
 *  @retval CUDNN_STATUS_BAD_PARAM
 *  @retval CUDNN_STATUS_INTERNAL_ERROR
 *  @retval CUDNN_STATUS_EXECUTION_FAILED
 *  @retval CUDNN_STATUS_NOT_SUPPORTED
 *  @since cuDNN 9.5.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnBackendUpdateCudaGraph(cudnnHandle_t handle,
                            cudnnBackendDescriptor_t executionPlan,
                            cudnnBackendDescriptor_t variantPack,
                            cudaGraph_t graph);

#if defined(__cplusplus)
}
#endif

#endif /* CUDNN_GRAPH_H_ */
