cudnn/include/cudnn_ops.h

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/**
 * @file cudnn_ops.h
 * @brief cuDNN Operations library - tensor descriptors, basic operations, batch normalization, pooling, etc.
 */

/*
 *  cudnn_ops : cuDNN's basic definitions and basic operations.
 */

#if !defined(CUDNN_OPS_H_)
#define CUDNN_OPS_H_

#include <stdint.h>

#include "cudnn_version.h"
#include "cudnn_graph.h"

/* These version numbers are autogenerated, do not edit manually. */
#define CUDNN_OPS_MAJOR 9
#define CUDNN_OPS_MINOR 22
#define CUDNN_OPS_PATCH 0

#if (CUDNN_OPS_MAJOR != CUDNN_MAJOR) || (CUDNN_OPS_MINOR != CUDNN_MINOR) || (CUDNN_OPS_PATCH != CUDNN_PATCHLEVEL)
#error Version mismatch in cuDNN OPS INFER!!!
#endif

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

/* Data structures to represent Image/Filter and the Neural Network Layer */

/** @brief Opaque descriptor for a tensor. @since cuDNN 9.0.0 */
typedef struct cudnnTensorStruct *cudnnTensorDescriptor_t;

/** @brief Opaque descriptor for a pooling operation. @since cuDNN 9.0.0 @deprecated Since cuDNN 9.0.0. Use graph API instead. */
typedef struct cudnnPoolingStruct *cudnnPoolingDescriptor_t CUDNN_DEPRECATED;

/** @brief Opaque descriptor for a filter (convolution kernel). @since cuDNN 9.0.0 @deprecated Since cuDNN 9.0.0. Use graph API instead. */
typedef struct cudnnFilterStruct *cudnnFilterDescriptor_t CUDNN_DEPRECATED;

/** @brief Opaque descriptor for Local Response Normalization (LRN). @since cuDNN 9.0.0 */
typedef struct cudnnLRNStruct *cudnnLRNDescriptor_t;

/** @brief Opaque descriptor for an activation function. @since cuDNN 9.0.0 @deprecated Since cuDNN 9.0.0. Use graph API instead. */
typedef struct cudnnActivationStruct *cudnnActivationDescriptor_t CUDNN_DEPRECATED;

/** @brief Opaque descriptor for a spatial transformer network. @since cuDNN 9.0.0 */
typedef struct cudnnSpatialTransformerStruct *cudnnSpatialTransformerDescriptor_t;

/** @brief Opaque descriptor for an element-wise tensor operation. @since cuDNN 9.0.0 @deprecated Since cuDNN 9.0.0. Use graph API instead. */
typedef struct cudnnOpTensorStruct *cudnnOpTensorDescriptor_t CUDNN_DEPRECATED;

/** @brief Opaque descriptor for a tensor reduction operation. @since cuDNN 9.0.0 @deprecated Since cuDNN 9.0.0. Use graph API instead. */
typedef struct cudnnReduceTensorStruct *cudnnReduceTensorDescriptor_t CUDNN_DEPRECATED;

/** @brief Opaque descriptor for a CTC loss function. @since cuDNN 9.0.0 */
typedef struct cudnnCTCLossStruct *cudnnCTCLossDescriptor_t;

/** @brief Opaque descriptor for tensor transform operations. @since cuDNN 9.0.0 @deprecated Since cuDNN 9.0.0. Use graph API instead. */
typedef struct cudnnTensorTransformStruct *cudnnTensorTransformDescriptor_t CUDNN_DEPRECATED;
/**
 * @brief Indicates whether results are guaranteed to be reproducible across runs.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_NON_DETERMINISTIC = 0, /**< Results may vary across runs. @since cuDNN 9.0.0 */
    CUDNN_DETERMINISTIC     = 1, /**< Results are guaranteed to be reproducible. @since cuDNN 9.0.0 */
} cudnnDeterminism_t;

/**
 * @brief Creates a tensor descriptor.
 *
 * Allocates and initializes a new tensor descriptor object.
 *
 * @param[out] tensorDesc  Pointer to the newly created tensor descriptor.
 *
 * @retval CUDNN_STATUS_SUCCESS           The descriptor was created successfully.
 * @retval CUDNN_STATUS_ALLOC_FAILED      Memory allocation failed.
 *
 * @since cuDNN 9.0.0
 * @see cudnnDestroyTensorDescriptor, cudnnSetTensor4dDescriptor
 */
cudnnStatus_t CUDNNWINAPI
cudnnCreateTensorDescriptor(cudnnTensorDescriptor_t *tensorDesc);

/**
 * @brief Sets a 4D tensor descriptor.
 *
 * Initializes a previously created tensor descriptor with the specified format,
 * data type, and dimensions. Strides are computed automatically based on the format.
 *
 * @param[in,out] tensorDesc  Tensor descriptor to initialize.
 * @param[in]     format      Memory layout format (e.g., NCHW or NHWC).
 * @param[in]     dataType    Data type of the tensor elements.
 * @param[in]     n           Number of images (batch size).
 * @param[in]     c           Number of feature maps (channels).
 * @param[in]     h           Height of each feature map.
 * @param[in]     w           Width of each feature map.
 *
 * @retval CUDNN_STATUS_SUCCESS           The descriptor was set successfully.
 * @retval CUDNN_STATUS_BAD_PARAM         An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @see cudnnSetTensor4dDescriptorEx, cudnnGetTensor4dDescriptor
 */
cudnnStatus_t CUDNNWINAPI
cudnnSetTensor4dDescriptor(cudnnTensorDescriptor_t tensorDesc,
                           cudnnTensorFormat_t format,
                           cudnnDataType_t dataType, /* image data type */
                           int n,                    /* number of inputs (batch size) */
                           int c,                    /* number of input feature maps */
                           int h,                    /* height of input section */
                           int w);                   /* width of input section */

/**
 * @brief Sets a 4D tensor descriptor with explicit strides.
 *
 * Initializes a previously created tensor descriptor with the specified data type,
 * dimensions, and explicit stride values for each dimension.
 *
 * @param[in,out] tensorDesc  Tensor descriptor to initialize.
 * @param[in]     dataType    Data type of the tensor elements.
 * @param[in]     n           Number of images (batch size).
 * @param[in]     c           Number of feature maps (channels).
 * @param[in]     h           Height of each feature map.
 * @param[in]     w           Width of each feature map.
 * @param[in]     nStride     Stride between images.
 * @param[in]     cStride     Stride between feature maps.
 * @param[in]     hStride     Stride between rows.
 * @param[in]     wStride     Stride between columns.
 *
 * @retval CUDNN_STATUS_SUCCESS           The descriptor was set successfully.
 * @retval CUDNN_STATUS_BAD_PARAM         An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @see cudnnSetTensor4dDescriptor, cudnnGetTensor4dDescriptor
 */
cudnnStatus_t CUDNNWINAPI
cudnnSetTensor4dDescriptorEx(cudnnTensorDescriptor_t tensorDesc,
                             cudnnDataType_t dataType, /* image data type */
                             int n,                    /* number of inputs (batch size) */
                             int c,                    /* number of input feature maps */
                             int h,                    /* height of input section */
                             int w,                    /* width of input section */
                             int nStride,
                             int cStride,
                             int hStride,
                             int wStride);

/**
 * @brief Retrieves the settings of a previously initialized 4D tensor descriptor.
 *
 * @param[in]  tensorDesc  Tensor descriptor to query.
 * @param[out] dataType    Data type of the tensor.
 * @param[out] n           Number of images (batch size).
 * @param[out] c           Number of feature maps (channels).
 * @param[out] h           Height of each feature map.
 * @param[out] w           Width of each feature map.
 * @param[out] nStride     Stride between images.
 * @param[out] cStride     Stride between feature maps.
 * @param[out] hStride     Stride between rows.
 * @param[out] wStride     Stride between columns.
 *
 * @retval CUDNN_STATUS_SUCCESS  The descriptor was queried successfully.
 *
 * @since cuDNN 9.0.0
 * @see cudnnSetTensor4dDescriptor
 */
cudnnStatus_t CUDNNWINAPI
cudnnGetTensor4dDescriptor(const cudnnTensorDescriptor_t tensorDesc,
                           cudnnDataType_t *dataType, /* image data type */
                           int *n,                    /* number of inputs (batch size) */
                           int *c,                    /* number of input feature maps  */
                           int *h,                    /* height of input section */
                           int *w,                    /* width of input section */
                           int *nStride,
                           int *cStride,
                           int *hStride,
                           int *wStride);

/**
 * @brief Sets an N-dimensional tensor descriptor.
 *
 * Initializes a tensor descriptor with arbitrary dimensionality, data type, dimensions, and strides.
 *
 * @param[in,out] tensorDesc  Tensor descriptor to initialize.
 * @param[in]     dataType    Data type of the tensor elements.
 * @param[in]     nbDims      Number of dimensions.
 * @param[in]     dimA        Array of dimension sizes (length nbDims).
 * @param[in]     strideA     Array of strides (length nbDims).
 *
 * @retval CUDNN_STATUS_SUCCESS     The descriptor was set successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @see cudnnGetTensorNdDescriptor
 */
cudnnStatus_t CUDNNWINAPI
cudnnSetTensorNdDescriptor(cudnnTensorDescriptor_t tensorDesc,
                           cudnnDataType_t dataType,
                           int nbDims,
                           const int dimA[],
                           const int strideA[]);

/**
 * @brief Sets an N-dimensional tensor descriptor with automatic stride computation.
 *
 * Initializes a tensor descriptor with the specified format; strides are computed
 * automatically from the format and dimensions.
 *
 * @param[in,out] tensorDesc  Tensor descriptor to initialize.
 * @param[in]     format      Memory layout format.
 * @param[in]     dataType    Data type of the tensor elements.
 * @param[in]     nbDims      Number of dimensions.
 * @param[in]     dimA        Array of dimension sizes (length nbDims).
 *
 * @retval CUDNN_STATUS_SUCCESS     The descriptor was set successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @see cudnnSetTensorNdDescriptor
 */
cudnnStatus_t CUDNNWINAPI
cudnnSetTensorNdDescriptorEx(cudnnTensorDescriptor_t tensorDesc,
                             cudnnTensorFormat_t format,
                             cudnnDataType_t dataType,
                             int nbDims,
                             const int dimA[]);

/**
 * @brief Retrieves the settings of a previously initialized N-dimensional tensor descriptor.
 *
 * @param[in]  tensorDesc      Tensor descriptor to query.
 * @param[in]  nbDimsRequested Number of dimensions to retrieve.
 * @param[out] dataType        Data type of the tensor.
 * @param[out] nbDims          Actual number of dimensions in the descriptor.
 * @param[out] dimA            Array to receive dimension sizes (length nbDimsRequested).
 * @param[out] strideA         Array to receive strides (length nbDimsRequested).
 *
 * @retval CUDNN_STATUS_SUCCESS  The descriptor was queried successfully.
 *
 * @since cuDNN 9.0.0
 * @see cudnnSetTensorNdDescriptor
 */
cudnnStatus_t CUDNNWINAPI
cudnnGetTensorNdDescriptor(const cudnnTensorDescriptor_t tensorDesc,
                           int nbDimsRequested,
                           cudnnDataType_t *dataType,
                           int *nbDims,
                           int dimA[],
                           int strideA[]);

/**
 * @brief Returns the memory size in bytes required by a tensor.
 *
 * @param[in]  tensorDesc  Tensor descriptor to query.
 * @param[out] size        Memory size in bytes.
 *
 * @retval CUDNN_STATUS_SUCCESS  The size was returned successfully.
 *
 * @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnGetTensorSizeInBytes(const cudnnTensorDescriptor_t tensorDesc, size_t *size);

/* PixelOffset( n, c, h, w ) = n *input_stride + c * feature_stride + h * h_stride + w * w_stride

   1)Example of all images in row major order one batch of features after the other (with an optional padding on row)
   input_stride :  c x h x h_stride
   feature_stride : h x h_stride
   h_stride  :  >= w  ( h_stride = w if no padding)
   w_stride  : 1


   2)Example of all images in row major with features maps interleaved
   input_stride :  c x h x h_stride
   feature_stride : 1
   h_stride  :  w x c
   w_stride  : c

   3)Example of all images in column major order one batch of features after the other (with optional padding on column)
   input_stride :  c x w x w_stride
   feature_stride : w x w_stride
   h_stride  :  1
   w_stride  :  >= h

*/

/**
 * @brief Destroys a tensor descriptor.
 *
 * Releases the resources associated with a tensor descriptor object.
 *
 * @param[in] tensorDesc  Tensor descriptor to destroy.
 *
 * @retval CUDNN_STATUS_SUCCESS  The descriptor was destroyed successfully.
 *
 * @since cuDNN 9.0.0
 * @see cudnnCreateTensorDescriptor
 */
cudnnStatus_t CUDNNWINAPI
cudnnDestroyTensorDescriptor(cudnnTensorDescriptor_t tensorDesc);

/**
 * @brief Specifies the direction for tensor transform folding operations.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_TRANSFORM_FOLD   = 0U, /**< Fold the tensor. @since cuDNN 9.0.0 */
    CUDNN_TRANSFORM_UNFOLD = 1U, /**< Unfold the tensor. @since cuDNN 9.0.0 */
} cudnnFoldingDirection_t;

/**
 * @brief Initializes the destination tensor descriptor for a tensor transform.
 *
 * Computes the destination tensor dimensions and size based on the transform and source descriptors.
 *
 * @param[in]     transformDesc   Transform descriptor specifying the operation.
 * @param[in]     srcDesc         Source tensor descriptor.
 * @param[in,out] destDesc        Destination tensor descriptor to be initialized.
 * @param[out]    destSizeInBytes Memory size in bytes of the destination tensor.
 *
 * @retval CUDNN_STATUS_SUCCESS     The destination descriptor was initialized successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnTransformTensorEx
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnInitTransformDest(const cudnnTensorTransformDescriptor_t transformDesc,
                       const cudnnTensorDescriptor_t srcDesc,
                       cudnnTensorDescriptor_t destDesc,
                       size_t *destSizeInBytes);

/**
 * @brief Creates a tensor transform descriptor.
 *
 * Allocates and initializes a new tensor transform descriptor object.
 *
 * @param[out] transformDesc  Pointer to the newly created transform descriptor.
 *
 * @retval CUDNN_STATUS_SUCCESS       The descriptor was created successfully.
 * @retval CUDNN_STATUS_ALLOC_FAILED  Memory allocation failed.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnDestroyTensorTransformDescriptor, cudnnSetTensorTransformDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnCreateTensorTransformDescriptor(cudnnTensorTransformDescriptor_t *transformDesc);

/**
 * @brief Configures a tensor transform descriptor.
 *
 * Sets the parameters of a previously created tensor transform descriptor including
 * padding, folding, and destination format.
 *
 * @param[in,out] transformDesc  Transform descriptor to configure.
 * @param[in]     nbDims         Number of dimensions.
 * @param[in]     destFormat     Destination tensor format.
 * @param[in]     padBeforeA     Array of padding values before each dimension.
 * @param[in]     padAfterA      Array of padding values after each dimension.
 * @param[in]     foldA          Array of fold parameters per dimension.
 * @param[in]     direction      Folding direction (fold or unfold).
 *
 * @retval CUDNN_STATUS_SUCCESS     The descriptor was configured successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnGetTensorTransformDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnSetTensorTransformDescriptor(cudnnTensorTransformDescriptor_t transformDesc,
                                  const uint32_t nbDims,
                                  const cudnnTensorFormat_t destFormat,
                                  const int32_t padBeforeA[],
                                  const int32_t padAfterA[],
                                  const uint32_t foldA[],
                                  const cudnnFoldingDirection_t direction);

/**
 * @brief Retrieves the settings of a previously initialized tensor transform descriptor.
 *
 * @param[in]  transformDesc   Transform descriptor to query.
 * @param[in]  nbDimsRequested Number of dimensions to retrieve.
 * @param[out] destFormat      Destination tensor format.
 * @param[out] padBeforeA      Array to receive pre-padding values.
 * @param[out] padAfterA       Array to receive post-padding values.
 * @param[out] foldA           Array to receive fold parameters.
 * @param[out] direction       Folding direction.
 *
 * @retval CUDNN_STATUS_SUCCESS  The descriptor was queried successfully.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnSetTensorTransformDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetTensorTransformDescriptor(cudnnTensorTransformDescriptor_t transformDesc,
                                  uint32_t nbDimsRequested,
                                  cudnnTensorFormat_t *destFormat,
                                  int32_t padBeforeA[],
                                  int32_t padAfterA[],
                                  uint32_t foldA[],
                                  cudnnFoldingDirection_t *direction);

/**
 * @brief Destroys a tensor transform descriptor.
 *
 * Releases the resources associated with a tensor transform descriptor.
 *
 * @param[in] transformDesc  Transform descriptor to destroy.
 *
 * @retval CUDNN_STATUS_SUCCESS  The descriptor was destroyed successfully.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnCreateTensorTransformDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnDestroyTensorTransformDescriptor(cudnnTensorTransformDescriptor_t transformDesc);

/**
 * @brief Copies and converts tensor data between layouts with alpha/beta blending.
 *
 * Performs y = alpha * x + beta * y, converting between tensor formats as needed.
 *
 * @param[in]     handle  cuDNN library handle.
 * @param[in]     alpha   Scaling factor for the source tensor.
 * @param[in]     xDesc   Source tensor descriptor.
 * @param[in]     x       Pointer to source tensor data.
 * @param[in]     beta    Scaling factor for the destination tensor.
 * @param[in]     yDesc   Destination tensor descriptor.
 * @param[in,out] y       Pointer to destination tensor data.
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnTransformTensorEx
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnTransformTensor(cudnnHandle_t handle,
                     const void *alpha,
                     const cudnnTensorDescriptor_t xDesc,
                     const void *x,
                     const void *beta,
                     const cudnnTensorDescriptor_t yDesc,
                     void *y);

/**
 * @brief Extended tensor transform with folding/padding support.
 *
 * Performs dest = alpha * transform(src) + beta * dest, using the specified
 * transform descriptor for padding and folding configuration.
 *
 * @param[in]     handle    cuDNN library handle.
 * @param[in]     transDesc Transform descriptor specifying the operation.
 * @param[in]     alpha     Scaling factor for the source tensor.
 * @param[in]     srcDesc   Source tensor descriptor.
 * @param[in]     srcData   Pointer to source tensor data.
 * @param[in]     beta      Scaling factor for the destination tensor.
 * @param[in]     destDesc  Destination tensor descriptor.
 * @param[in,out] destData  Pointer to destination tensor data.
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnTransformTensor, cudnnSetTensorTransformDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnTransformTensorEx(cudnnHandle_t handle,
                       const cudnnTensorTransformDescriptor_t transDesc,
                       const void *alpha,
                       const cudnnTensorDescriptor_t srcDesc,
                       const void *srcData,
                       const void *beta,
                       const cudnnTensorDescriptor_t destDesc,
                       void *destData);

/**
 * @brief Adds a scaled bias tensor to a destination tensor with broadcasting.
 *
 * Performs C = alpha * A + beta * C, where A is broadcast to match C dimensions.
 *
 * @param[in]     handle  cuDNN library handle.
 * @param[in]     alpha   Scaling factor for the bias tensor A.
 * @param[in]     aDesc   Bias tensor descriptor.
 * @param[in]     A       Pointer to bias tensor data.
 * @param[in]     beta    Scaling factor for the destination tensor C.
 * @param[in]     cDesc   Destination tensor descriptor.
 * @param[in,out] C       Pointer to destination tensor data.
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnAddTensor(cudnnHandle_t handle,
               const void *alpha,
               const cudnnTensorDescriptor_t aDesc,
               const void *A,
               const void *beta,
               const cudnnTensorDescriptor_t cDesc,
               void *C);

/**
 * @brief Enumerates the element-wise tensor operations supported by cudnnOpTensor.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_OP_TENSOR_ADD  = 0, /**< Element-wise addition. @since cuDNN 9.0.0 */
    CUDNN_OP_TENSOR_MUL  = 1, /**< Element-wise multiplication. @since cuDNN 9.0.0 */
    CUDNN_OP_TENSOR_MIN  = 2, /**< Element-wise minimum. @since cuDNN 9.0.0 */
    CUDNN_OP_TENSOR_MAX  = 3, /**< Element-wise maximum. @since cuDNN 9.0.0 */
    CUDNN_OP_TENSOR_SQRT = 4, /**< Element-wise square root (unary, B tensor ignored). @since cuDNN 9.0.0 */
    CUDNN_OP_TENSOR_NOT  = 5, /**< Element-wise logical NOT (unary, B tensor ignored). @since cuDNN 9.0.0 */
} cudnnOpTensorOp_t;

/**
 * @brief Creates an op tensor descriptor.
 *
 * @param[out] opTensorDesc  Pointer to the newly created op tensor descriptor.
 *
 * @retval CUDNN_STATUS_SUCCESS       The descriptor was created successfully.
 * @retval CUDNN_STATUS_ALLOC_FAILED  Memory allocation failed.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnDestroyOpTensorDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnCreateOpTensorDescriptor(cudnnOpTensorDescriptor_t *opTensorDesc);

/**
 * @brief Configures an op tensor descriptor.
 *
 * @param[in,out] opTensorDesc     Op tensor descriptor to configure.
 * @param[in]     opTensorOp       Tensor operation to perform.
 * @param[in]     opTensorCompType Computation data type.
 * @param[in]     opTensorNanOpt   NaN propagation policy.
 *
 * @retval CUDNN_STATUS_SUCCESS     The descriptor was set successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnGetOpTensorDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnSetOpTensorDescriptor(cudnnOpTensorDescriptor_t opTensorDesc,
                           cudnnOpTensorOp_t opTensorOp,
                           cudnnDataType_t opTensorCompType,
                           cudnnNanPropagation_t opTensorNanOpt);

/**
 * @brief Retrieves the settings of an op tensor descriptor.
 *
 * @param[in]  opTensorDesc     Op tensor descriptor to query.
 * @param[out] opTensorOp       Tensor operation type.
 * @param[out] opTensorCompType Computation data type.
 * @param[out] opTensorNanOpt   NaN propagation policy.
 *
 * @retval CUDNN_STATUS_SUCCESS  The descriptor was queried successfully.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnSetOpTensorDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetOpTensorDescriptor(const cudnnOpTensorDescriptor_t opTensorDesc,
                           cudnnOpTensorOp_t *opTensorOp,
                           cudnnDataType_t *opTensorCompType,
                           cudnnNanPropagation_t *opTensorNanOpt);

/**
 * @brief Destroys an op tensor descriptor.
 *
 * @param[in] opTensorDesc  Op tensor descriptor to destroy.
 *
 * @retval CUDNN_STATUS_SUCCESS  The descriptor was destroyed successfully.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnCreateOpTensorDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnDestroyOpTensorDescriptor(cudnnOpTensorDescriptor_t opTensorDesc);

/**
 * @brief Performs element-wise tensor operations.
 *
 * Computes C = op(alpha1 * A, alpha2 * B) + beta * C. The B tensor is ignored
 * for CUDNN_OP_TENSOR_SQRT and CUDNN_OP_TENSOR_NOT (unary operations).
 *
 * @param[in]     handle       cuDNN library handle.
 * @param[in]     opTensorDesc Op tensor descriptor specifying the operation.
 * @param[in]     alpha1       Scaling factor for tensor A.
 * @param[in]     aDesc        Descriptor for tensor A.
 * @param[in]     A            Pointer to tensor A data.
 * @param[in]     alpha2       Scaling factor for tensor B.
 * @param[in]     bDesc        Descriptor for tensor B.
 * @param[in]     B            Pointer to tensor B data.
 * @param[in]     beta         Scaling factor for tensor C.
 * @param[in]     cDesc        Descriptor for tensor C.
 * @param[in,out] C            Pointer to tensor C data.
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnSetOpTensorDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnOpTensor(cudnnHandle_t handle,
              const cudnnOpTensorDescriptor_t opTensorDesc,
              const void *alpha1,
              const cudnnTensorDescriptor_t aDesc,
              const void *A,
              const void *alpha2,
              const cudnnTensorDescriptor_t bDesc,
              const void *B,
              const void *beta,
              const cudnnTensorDescriptor_t cDesc,
              void *C);

/**
 * @brief Specifies whether indices are computed during a reduction operation.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_REDUCE_TENSOR_NO_INDICES        = 0, /**< Do not compute indices. @since cuDNN 9.0.0 */
    CUDNN_REDUCE_TENSOR_FLATTENED_INDICES = 1, /**< Compute flattened indices of min/max values. @since cuDNN 9.0.0 */
} cudnnReduceTensorIndices_t CUDNN_DEPRECATED;

/**
 * @brief Data type used for reduction indices (all unsigned).
 *
 * Currently only 32-bit unsigned is fully supported; other sizes are reserved.
 *
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_32BIT_INDICES = 0, /**< 32-bit unsigned indices. @since cuDNN 9.0.0 */
    CUDNN_64BIT_INDICES = 1, /**< 64-bit unsigned indices. @since cuDNN 9.0.0 */
    CUDNN_16BIT_INDICES = 2, /**< 16-bit unsigned indices. @since cuDNN 9.0.0 */
    CUDNN_8BIT_INDICES  = 3, /**< 8-bit unsigned indices. @since cuDNN 9.0.0 */
} cudnnIndicesType_t CUDNN_DEPRECATED;

/**
 * @brief Creates a reduce tensor descriptor.
 *
 * @param[out] reduceTensorDesc  Pointer to the newly created reduce tensor descriptor.
 *
 * @retval CUDNN_STATUS_SUCCESS       The descriptor was created successfully.
 * @retval CUDNN_STATUS_ALLOC_FAILED  Memory allocation failed.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnDestroyReduceTensorDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnCreateReduceTensorDescriptor(cudnnReduceTensorDescriptor_t *reduceTensorDesc);

/**
 * @brief Configures a reduce tensor descriptor.
 *
 * @param[in,out] reduceTensorDesc        Reduce tensor descriptor to configure.
 * @param[in]     reduceTensorOp          Reduction operation to perform.
 * @param[in]     reduceTensorCompType    Computation data type.
 * @param[in]     reduceTensorNanOpt      NaN propagation policy (applies to min/max only).
 * @param[in]     reduceTensorIndices     Whether to compute indices.
 * @param[in]     reduceTensorIndicesType Data type for computed indices.
 *
 * @retval CUDNN_STATUS_SUCCESS     The descriptor was set successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnGetReduceTensorDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnSetReduceTensorDescriptor(cudnnReduceTensorDescriptor_t reduceTensorDesc,
                               cudnnReduceTensorOp_t reduceTensorOp,
                               cudnnDataType_t reduceTensorCompType,
                               cudnnNanPropagation_t reduceTensorNanOpt,
                               cudnnReduceTensorIndices_t reduceTensorIndices,
                               cudnnIndicesType_t reduceTensorIndicesType);

/**
 * @brief Retrieves the settings of a reduce tensor descriptor.
 *
 * @param[in]  reduceTensorDesc        Reduce tensor descriptor to query.
 * @param[out] reduceTensorOp          Reduction operation type.
 * @param[out] reduceTensorCompType    Computation data type.
 * @param[out] reduceTensorNanOpt      NaN propagation policy.
 * @param[out] reduceTensorIndices     Whether indices are computed.
 * @param[out] reduceTensorIndicesType Data type for computed indices.
 *
 * @retval CUDNN_STATUS_SUCCESS  The descriptor was queried successfully.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnSetReduceTensorDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetReduceTensorDescriptor(const cudnnReduceTensorDescriptor_t reduceTensorDesc,
                               cudnnReduceTensorOp_t *reduceTensorOp,
                               cudnnDataType_t *reduceTensorCompType,
                               cudnnNanPropagation_t *reduceTensorNanOpt,
                               cudnnReduceTensorIndices_t *reduceTensorIndices,
                               cudnnIndicesType_t *reduceTensorIndicesType);

/**
 * @brief Destroys a reduce tensor descriptor.
 *
 * @param[in] reduceTensorDesc  Reduce tensor descriptor to destroy.
 *
 * @retval CUDNN_STATUS_SUCCESS  The descriptor was destroyed successfully.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnCreateReduceTensorDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnDestroyReduceTensorDescriptor(cudnnReduceTensorDescriptor_t reduceTensorDesc);

/**
 * @brief Returns the minimum size of the index space for a reduction operation.
 *
 * @param[in]  handle           cuDNN library handle.
 * @param[in]  reduceTensorDesc Reduce tensor descriptor.
 * @param[in]  aDesc            Input tensor descriptor.
 * @param[in]  cDesc            Output tensor descriptor.
 * @param[out] sizeInBytes      Minimum index space size in bytes.
 *
 * @retval CUDNN_STATUS_SUCCESS  The size was returned successfully.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnReduceTensor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetReductionIndicesSize(cudnnHandle_t handle,
                             const cudnnReduceTensorDescriptor_t reduceTensorDesc,
                             const cudnnTensorDescriptor_t aDesc,
                             const cudnnTensorDescriptor_t cDesc,
                             size_t *sizeInBytes);

/**
 * @brief Returns the minimum workspace size required for a reduction operation.
 *
 * @param[in]  handle           cuDNN library handle.
 * @param[in]  reduceTensorDesc Reduce tensor descriptor.
 * @param[in]  aDesc            Input tensor descriptor.
 * @param[in]  cDesc            Output tensor descriptor.
 * @param[out] sizeInBytes      Minimum workspace size in bytes.
 *
 * @retval CUDNN_STATUS_SUCCESS  The size was returned successfully.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnReduceTensor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetReductionWorkspaceSize(cudnnHandle_t handle,
                               const cudnnReduceTensorDescriptor_t reduceTensorDesc,
                               const cudnnTensorDescriptor_t aDesc,
                               const cudnnTensorDescriptor_t cDesc,
                               size_t *sizeInBytes);

/**
 * @brief Performs a tensor reduction operation.
 *
 * Computes C = reduce_op(alpha * A) + beta * C. NaN propagation applies only
 * to min and max operations. The indices space is ignored for operations other
 * than min or max.
 *
 * @param[in]     handle              cuDNN library handle.
 * @param[in]     reduceTensorDesc    Reduce tensor descriptor.
 * @param[out]    indices             Pointer to index space (for min/max ops).
 * @param[in]     indicesSizeInBytes  Size of the index space in bytes.
 * @param[out]    workspace           Pointer to workspace memory.
 * @param[in]     workspaceSizeInBytes Size of the workspace in bytes.
 * @param[in]     alpha               Scaling factor for the input tensor.
 * @param[in]     aDesc               Input tensor descriptor.
 * @param[in]     A                   Pointer to input tensor data.
 * @param[in]     beta                Scaling factor for the output tensor.
 * @param[in]     cDesc               Output tensor descriptor.
 * @param[in,out] C                   Pointer to output tensor data.
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnGetReductionWorkspaceSize, cudnnGetReductionIndicesSize
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnReduceTensor(cudnnHandle_t handle,
                  const cudnnReduceTensorDescriptor_t reduceTensorDesc,
                  void *indices,
                  size_t indicesSizeInBytes,
                  void *workspace,
                  size_t workspaceSizeInBytes,
                  const void *alpha,
                  const cudnnTensorDescriptor_t aDesc,
                  const void *A,
                  const void *beta,
                  const cudnnTensorDescriptor_t cDesc,
                  void *C);

/**
 * @brief Fills a tensor with a constant value.
 *
 * Sets every element of the tensor to the specified value: y[i] = value[0].
 *
 * @param[in]  handle    cuDNN library handle.
 * @param[in]  yDesc     Tensor descriptor.
 * @param[out] y         Pointer to tensor data.
 * @param[in]  valuePtr  Pointer to the fill value (type matches tensor data type).
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @see cudnnScaleTensor
 */
cudnnStatus_t CUDNNWINAPI
cudnnSetTensor(cudnnHandle_t handle, const cudnnTensorDescriptor_t yDesc, void *y, const void *valuePtr);

/**
 * @brief Scales all elements of a tensor by a constant factor.
 *
 * Performs y[i] = alpha * y[i] for every element.
 *
 * @param[in]     handle  cuDNN library handle.
 * @param[in]     yDesc   Tensor descriptor.
 * @param[in,out] y       Pointer to tensor data.
 * @param[in]     alpha   Scaling factor (type matches tensor computation type).
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnSetTensor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnScaleTensor(cudnnHandle_t handle, const cudnnTensorDescriptor_t yDesc, void *y, const void *alpha);

/**
 * @brief Creates a filter descriptor.
 *
 * Allocates and initializes a new filter (convolution kernel) descriptor.
 *
 * @param[out] filterDesc  Pointer to the newly created filter descriptor.
 *
 * @retval CUDNN_STATUS_SUCCESS       The descriptor was created successfully.
 * @retval CUDNN_STATUS_ALLOC_FAILED  Memory allocation failed.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnDestroyFilterDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnCreateFilterDescriptor(cudnnFilterDescriptor_t *filterDesc);

/**
 * @brief Sets a 4D filter descriptor.
 *
 * Initializes a filter descriptor with the specified data type, format, and dimensions.
 *
 * @param[in,out] filterDesc  Filter descriptor to initialize.
 * @param[in]     dataType    Data type of the filter elements.
 * @param[in]     format      Memory layout format.
 * @param[in]     k           Number of output feature maps.
 * @param[in]     c           Number of input feature maps.
 * @param[in]     h           Height of each filter.
 * @param[in]     w           Width of each filter.
 *
 * @retval CUDNN_STATUS_SUCCESS     The descriptor was set successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnGetFilter4dDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnSetFilter4dDescriptor(cudnnFilterDescriptor_t filterDesc,
                           cudnnDataType_t dataType, /* image data type */
                           cudnnTensorFormat_t format,
                           int k,  /* number of output feature maps */
                           int c,  /* number of input feature maps */
                           int h,  /* height of each input filter */
                           int w); /* width of  each input filter */

/**
 * @brief Retrieves the settings of a 4D filter descriptor.
 *
 * @param[in]  filterDesc  Filter descriptor to query.
 * @param[out] dataType    Data type of the filter.
 * @param[out] format      Memory layout format.
 * @param[out] k           Number of output feature maps.
 * @param[out] c           Number of input feature maps.
 * @param[out] h           Height of each filter.
 * @param[out] w           Width of each filter.
 *
 * @retval CUDNN_STATUS_SUCCESS  The descriptor was queried successfully.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnSetFilter4dDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetFilter4dDescriptor(const cudnnFilterDescriptor_t filterDesc,
                           cudnnDataType_t *dataType, /* image data type */
                           cudnnTensorFormat_t *format,
                           int *k,  /* number of output feature maps */
                           int *c,  /* number of input feature maps */
                           int *h,  /* height of each input filter */
                           int *w); /* width of  each input filter */

/**
 * @brief Sets an N-dimensional filter descriptor.
 *
 * @param[in,out] filterDesc  Filter descriptor to initialize.
 * @param[in]     dataType    Data type of the filter elements.
 * @param[in]     format      Memory layout format.
 * @param[in]     nbDims      Number of dimensions.
 * @param[in]     filterDimA  Array of filter dimension sizes (length nbDims).
 *
 * @retval CUDNN_STATUS_SUCCESS     The descriptor was set successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnGetFilterNdDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnSetFilterNdDescriptor(cudnnFilterDescriptor_t filterDesc,
                           cudnnDataType_t dataType, /* image data type */
                           cudnnTensorFormat_t format,
                           int nbDims,
                           const int filterDimA[]);

/**
 * @brief Retrieves the settings of an N-dimensional filter descriptor.
 *
 * @param[in]  filterDesc      Filter descriptor to query.
 * @param[in]  nbDimsRequested Number of dimensions to retrieve.
 * @param[out] dataType        Data type of the filter.
 * @param[out] format          Memory layout format.
 * @param[out] nbDims          Actual number of dimensions.
 * @param[out] filterDimA      Array to receive dimension sizes.
 *
 * @retval CUDNN_STATUS_SUCCESS  The descriptor was queried successfully.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnSetFilterNdDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetFilterNdDescriptor(const cudnnFilterDescriptor_t filterDesc,
                           int nbDimsRequested,
                           cudnnDataType_t *dataType, /* image data type */
                           cudnnTensorFormat_t *format,
                           int *nbDims,
                           int filterDimA[]);

/**
 * @brief Returns the memory size in bytes required by a filter.
 *
 * @param[in]  filterDesc  Filter descriptor to query.
 * @param[out] size        Memory size in bytes.
 *
 * @retval CUDNN_STATUS_SUCCESS  The size was returned successfully.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetFilterSizeInBytes(const cudnnFilterDescriptor_t filterDesc, size_t *size);

/**
 * @brief Transforms filter data between layouts.
 *
 * Converts filter data from one format to another using the specified transform descriptor.
 *
 * @param[in]     handle    cuDNN library handle.
 * @param[in]     transDesc Transform descriptor specifying the operation.
 * @param[in]     alpha     Scaling factor for the source filter.
 * @param[in]     srcDesc   Source filter descriptor.
 * @param[in]     srcData   Pointer to source filter data.
 * @param[in]     beta      Scaling factor for the destination filter.
 * @param[in]     destDesc  Destination filter descriptor.
 * @param[in,out] destData  Pointer to destination filter data.
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnTransformTensorEx
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnTransformFilter(cudnnHandle_t handle,
                     const cudnnTensorTransformDescriptor_t transDesc,
                     const void *alpha,
                     const cudnnFilterDescriptor_t srcDesc,
                     const void *srcData,
                     const void *beta,
                     const cudnnFilterDescriptor_t destDesc,
                     void *destData);

/**
 * @brief Destroys a filter descriptor.
 *
 * @param[in] filterDesc  Filter descriptor to destroy.
 *
 * @retval CUDNN_STATUS_SUCCESS  The descriptor was destroyed successfully.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnCreateFilterDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnDestroyFilterDescriptor(cudnnFilterDescriptor_t filterDesc);

/**
 * @brief Selects the softmax implementation algorithm.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_SOFTMAX_FAST     = 0, /**< Straightforward softmax without overflow protection. @since cuDNN 9.0.0 */
    CUDNN_SOFTMAX_ACCURATE = 1, /**< Scales by max value to avoid floating-point overflow. @since cuDNN 9.0.0 */
    CUDNN_SOFTMAX_LOG      = 2 /**< Log-softmax with max-value scaling for overflow protection. @since cuDNN 9.0.0 */
} cudnnSoftmaxAlgorithm_t;

/**
 * @brief Selects the scope over which the softmax computation is performed.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_SOFTMAX_MODE_INSTANCE = 0, /**< Compute softmax over all C, H, W for each image (N). @since cuDNN 9.0.0 */
    CUDNN_SOFTMAX_MODE_CHANNEL  = 1  /**< Compute softmax over channel (C) for each spatial location (H, W) and image (N). @since cuDNN 9.0.0 */
} cudnnSoftmaxMode_t;

/* Softmax functions: All of the form "output = alpha * Op(inputs) + beta * output" */

/**
 * @brief Performs forward softmax computation.
 *
 * Computes y = alpha * softmax(x) + beta * y.
 *
 * @param[in]     handle  cuDNN library handle.
 * @param[in]     algo    Softmax algorithm to use.
 * @param[in]     mode    Softmax computation scope.
 * @param[in]     alpha   Scaling factor for the result.
 * @param[in]     xDesc   Input tensor descriptor.
 * @param[in]     x       Pointer to input tensor data.
 * @param[in]     beta    Scaling factor for the destination tensor.
 * @param[in]     yDesc   Output tensor descriptor.
 * @param[in,out] y       Pointer to output tensor data.
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @see cudnnSoftmaxBackward
 */
cudnnStatus_t CUDNNWINAPI
cudnnSoftmaxForward(cudnnHandle_t handle,
                    cudnnSoftmaxAlgorithm_t algo,
                    cudnnSoftmaxMode_t mode,
                    const void *alpha,
                    const cudnnTensorDescriptor_t xDesc,
                    const void *x,
                    const void *beta,
                    const cudnnTensorDescriptor_t yDesc,
                    void *y);

/**
 * @brief Selects the pooling method used in pooling operations.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_POOLING_MAX                           = 0, /**< Maximum value in the pooling window. @since cuDNN 9.0.0 */
    CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING = 1, /**< Average pooling; element count includes padded positions. @since cuDNN 9.0.0 */
    CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING = 2, /**< Average pooling; element count excludes padded positions. @since cuDNN 9.0.0 */
    CUDNN_POOLING_MAX_DETERMINISTIC             = 3 /**< Deterministic max pooling (reproducible results). @since cuDNN 9.0.0 */
} cudnnPoolingMode_t CUDNN_DEPRECATED;

/**
 * @brief Creates a pooling descriptor.
 *
 * @param[out] poolingDesc  Pointer to the newly created pooling descriptor.
 *
 * @retval CUDNN_STATUS_SUCCESS       The descriptor was created successfully.
 * @retval CUDNN_STATUS_ALLOC_FAILED  Memory allocation failed.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnDestroyPoolingDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnCreatePoolingDescriptor(cudnnPoolingDescriptor_t *poolingDesc);

/**
 * @brief Configures a 2D pooling descriptor.
 *
 * @param[in,out] poolingDesc       Pooling descriptor to configure.
 * @param[in]     mode              Pooling mode (max, average, etc.).
 * @param[in]     maxpoolingNanOpt  NaN propagation policy for max pooling.
 * @param[in]     windowHeight      Height of the pooling window.
 * @param[in]     windowWidth       Width of the pooling window.
 * @param[in]     verticalPadding   Vertical padding size.
 * @param[in]     horizontalPadding Horizontal padding size.
 * @param[in]     verticalStride    Vertical stride.
 * @param[in]     horizontalStride  Horizontal stride.
 *
 * @retval CUDNN_STATUS_SUCCESS     The descriptor was set successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnGetPooling2dDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnSetPooling2dDescriptor(cudnnPoolingDescriptor_t poolingDesc,
                            cudnnPoolingMode_t mode,
                            cudnnNanPropagation_t maxpoolingNanOpt,
                            int windowHeight,
                            int windowWidth,
                            int verticalPadding,
                            int horizontalPadding,
                            int verticalStride,
                            int horizontalStride);

/**
 * @brief Retrieves the settings of a 2D pooling descriptor.
 *
 * @param[in]  poolingDesc       Pooling descriptor to query.
 * @param[out] mode              Pooling mode.
 * @param[out] maxpoolingNanOpt  NaN propagation policy.
 * @param[out] windowHeight      Height of the pooling window.
 * @param[out] windowWidth       Width of the pooling window.
 * @param[out] verticalPadding   Vertical padding size.
 * @param[out] horizontalPadding Horizontal padding size.
 * @param[out] verticalStride    Vertical stride.
 * @param[out] horizontalStride  Horizontal stride.
 *
 * @retval CUDNN_STATUS_SUCCESS  The descriptor was queried successfully.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnSetPooling2dDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetPooling2dDescriptor(const cudnnPoolingDescriptor_t poolingDesc,
                            cudnnPoolingMode_t *mode,
                            cudnnNanPropagation_t *maxpoolingNanOpt,
                            int *windowHeight,
                            int *windowWidth,
                            int *verticalPadding,
                            int *horizontalPadding,
                            int *verticalStride,
                            int *horizontalStride);

/**
 * @brief Configures an N-dimensional pooling descriptor.
 *
 * @param[in,out] poolingDesc     Pooling descriptor to configure.
 * @param[in]     mode            Pooling mode.
 * @param[in]     maxpoolingNanOpt NaN propagation policy for max pooling.
 * @param[in]     nbDims          Number of dimensions.
 * @param[in]     windowDimA      Array of pooling window sizes (length nbDims).
 * @param[in]     paddingA        Array of padding sizes (length nbDims).
 * @param[in]     strideA         Array of strides (length nbDims).
 *
 * @retval CUDNN_STATUS_SUCCESS     The descriptor was set successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnGetPoolingNdDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnSetPoolingNdDescriptor(cudnnPoolingDescriptor_t poolingDesc,
                            const cudnnPoolingMode_t mode,
                            const cudnnNanPropagation_t maxpoolingNanOpt,
                            int nbDims,
                            const int windowDimA[],
                            const int paddingA[],
                            const int strideA[]);

/**
 * @brief Retrieves the settings of an N-dimensional pooling descriptor.
 *
 * @param[in]  poolingDesc     Pooling descriptor to query.
 * @param[in]  nbDimsRequested Number of dimensions to retrieve.
 * @param[out] mode            Pooling mode.
 * @param[out] maxpoolingNanOpt NaN propagation policy.
 * @param[out] nbDims          Actual number of dimensions.
 * @param[out] windowDimA      Array to receive window sizes.
 * @param[out] paddingA        Array to receive padding sizes.
 * @param[out] strideA         Array to receive strides.
 *
 * @retval CUDNN_STATUS_SUCCESS  The descriptor was queried successfully.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnSetPoolingNdDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetPoolingNdDescriptor(const cudnnPoolingDescriptor_t poolingDesc,
                            int nbDimsRequested,
                            cudnnPoolingMode_t *mode,
                            cudnnNanPropagation_t *maxpoolingNanOpt,
                            int *nbDims,
                            int windowDimA[],
                            int paddingA[],
                            int strideA[]);

/**
 * @brief Computes the output dimensions of an N-dimensional pooling operation.
 *
 * @param[in]  poolingDesc      Pooling descriptor.
 * @param[in]  inputTensorDesc  Input tensor descriptor.
 * @param[in]  nbDims           Number of dimensions.
 * @param[out] outputTensorDimA Array to receive output dimension sizes.
 *
 * @retval CUDNN_STATUS_SUCCESS     The dimensions were computed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetPoolingNdForwardOutputDim(const cudnnPoolingDescriptor_t poolingDesc,
                                  const cudnnTensorDescriptor_t inputTensorDesc,
                                  int nbDims,
                                  int outputTensorDimA[]);

/**
 * @brief Computes the output dimensions of a 2D pooling operation.
 *
 * @param[in]  poolingDesc     Pooling descriptor.
 * @param[in]  inputTensorDesc Input tensor descriptor.
 * @param[out] n               Output batch size.
 * @param[out] c               Output number of channels.
 * @param[out] h               Output height.
 * @param[out] w               Output width.
 *
 * @retval CUDNN_STATUS_SUCCESS     The dimensions were computed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetPooling2dForwardOutputDim(const cudnnPoolingDescriptor_t poolingDesc,
                                  const cudnnTensorDescriptor_t inputTensorDesc,
                                  int *n,
                                  int *c,
                                  int *h,
                                  int *w);

/**
 * @brief Destroys a pooling descriptor.
 *
 * @param[in] poolingDesc  Pooling descriptor to destroy.
 *
 * @retval CUDNN_STATUS_SUCCESS  The descriptor was destroyed successfully.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnCreatePoolingDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnDestroyPoolingDescriptor(cudnnPoolingDescriptor_t poolingDesc);

/* Pooling functions: All of the form "output = alpha * Op(inputs) + beta * output" */

/**
 * @brief Performs forward pooling.
 *
 * Computes y = alpha * pool(x) + beta * y.
 *
 * @param[in]     handle      cuDNN library handle.
 * @param[in]     poolingDesc Pooling descriptor.
 * @param[in]     alpha       Scaling factor for the pooling result.
 * @param[in]     xDesc       Input tensor descriptor.
 * @param[in]     x           Pointer to input tensor data.
 * @param[in]     beta        Scaling factor for the destination tensor.
 * @param[in]     yDesc       Output tensor descriptor.
 * @param[in,out] y           Pointer to output tensor data.
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnPoolingBackward
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnPoolingForward(cudnnHandle_t handle,
                    const cudnnPoolingDescriptor_t poolingDesc,
                    const void *alpha,
                    const cudnnTensorDescriptor_t xDesc,
                    const void *x,
                    const void *beta,
                    const cudnnTensorDescriptor_t yDesc,
                    void *y);

/* Activation functions: All of the form "output = alpha * Op(inputs) + beta * output" */

/**
 * @brief Creates an activation descriptor.
 *
 * @param[out] activationDesc  Pointer to the newly created activation descriptor.
 *
 * @retval CUDNN_STATUS_SUCCESS       The descriptor was created successfully.
 * @retval CUDNN_STATUS_ALLOC_FAILED  Memory allocation failed.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnDestroyActivationDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnCreateActivationDescriptor(cudnnActivationDescriptor_t *activationDesc);

/**
 * @brief Configures an activation descriptor.
 *
 * @param[in,out] activationDesc  Activation descriptor to configure.
 * @param[in]     mode            Activation function type.
 * @param[in]     reluNanOpt      NaN propagation policy for ReLU.
 * @param[in]     coef            Ceiling for clipped ReLU, or alpha for ELU.
 *
 * @retval CUDNN_STATUS_SUCCESS     The descriptor was set successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnGetActivationDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnSetActivationDescriptor(cudnnActivationDescriptor_t activationDesc,
                             cudnnActivationMode_t mode,
                             cudnnNanPropagation_t reluNanOpt,
                             double coef); /* ceiling for clipped RELU, alpha for ELU */

/**
 * @brief Retrieves the settings of an activation descriptor.
 *
 * @param[in]  activationDesc  Activation descriptor to query.
 * @param[out] mode            Activation function type.
 * @param[out] reluNanOpt      NaN propagation policy.
 * @param[out] coef            Ceiling for clipped ReLU, or alpha for ELU.
 *
 * @retval CUDNN_STATUS_SUCCESS  The descriptor was queried successfully.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnSetActivationDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetActivationDescriptor(const cudnnActivationDescriptor_t activationDesc,
                             cudnnActivationMode_t *mode,
                             cudnnNanPropagation_t *reluNanOpt,
                             double *coef); /* ceiling for clipped RELU, alpha for ELU */

/**
 * @brief Sets the beta parameter for Swish activation.
 *
 * @param[in,out] activationDesc  Activation descriptor to modify.
 * @param[in]     swish_beta      Beta value for the Swish activation function.
 *
 * @retval CUDNN_STATUS_SUCCESS     The parameter was set successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnGetActivationDescriptorSwishBeta
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnSetActivationDescriptorSwishBeta(cudnnActivationDescriptor_t activationDesc, double swish_beta);

/**
 * @brief Retrieves the beta parameter for Swish activation.
 *
 * @param[in]  activationDesc  Activation descriptor to query.
 * @param[out] swish_beta      Beta value for the Swish activation function.
 *
 * @retval CUDNN_STATUS_SUCCESS  The parameter was retrieved successfully.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnSetActivationDescriptorSwishBeta
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetActivationDescriptorSwishBeta(cudnnActivationDescriptor_t activationDesc, double *swish_beta);

/**
 * @brief Destroys an activation descriptor.
 *
 * @param[in] activationDesc  Activation descriptor to destroy.
 *
 * @retval CUDNN_STATUS_SUCCESS  The descriptor was destroyed successfully.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnCreateActivationDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnDestroyActivationDescriptor(cudnnActivationDescriptor_t activationDesc);

/**
 * @brief Performs forward activation.
 *
 * Computes y = alpha * activation(x) + beta * y.
 *
 * @param[in]     handle          cuDNN library handle.
 * @param[in]     activationDesc  Activation descriptor.
 * @param[in]     alpha           Scaling factor for the activation result.
 * @param[in]     xDesc           Input tensor descriptor.
 * @param[in]     x               Pointer to input tensor data.
 * @param[in]     beta            Scaling factor for the destination tensor.
 * @param[in]     yDesc           Output tensor descriptor.
 * @param[in,out] y               Pointer to output tensor data.
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnActivationBackward
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnActivationForward(cudnnHandle_t handle,
                       cudnnActivationDescriptor_t activationDesc,
                       const void *alpha,
                       const cudnnTensorDescriptor_t xDesc,
                       const void *x,
                       const void *beta,
                       const cudnnTensorDescriptor_t yDesc,
                       void *y);

/**
 * @brief Creates a Local Response Normalization (LRN) descriptor.
 *
 * Uses lrnN=5, lrnAlpha=1e-4, lrnBeta=0.75, lrnK=2.0 as defaults from
 * Krizhevsky'12 ImageNet paper.
 *
 * @param[out] normDesc  Pointer to the newly created LRN descriptor.
 *
 * @retval CUDNN_STATUS_SUCCESS       The descriptor was created successfully.
 * @retval CUDNN_STATUS_ALLOC_FAILED  Memory allocation failed.
 *
 * @since cuDNN 9.0.0
 * @see cudnnDestroyLRNDescriptor, cudnnSetLRNDescriptor
 */
cudnnStatus_t CUDNNWINAPI
cudnnCreateLRNDescriptor(cudnnLRNDescriptor_t *normDesc);

#define CUDNN_LRN_MIN_N 1       /* minimum allowed lrnN */
#define CUDNN_LRN_MAX_N 16      /* maximum allowed lrnN */
#define CUDNN_LRN_MIN_K 1e-5    /* minimum allowed lrnK */
#define CUDNN_LRN_MIN_BETA 0.01 /* minimum allowed lrnBeta */

/**
 * @brief Selects the Local Response Normalization (LRN) mode.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_LRN_CROSS_CHANNEL_DIM1 = 0, /**< LRN computed across tensor dimension dimA[1]. @since cuDNN 9.0.0 */
} cudnnLRNMode_t;

/**
 * @brief Configures an LRN descriptor.
 *
 * Uses a window [center-lookBehind, center+lookAhead], where
 * lookBehind = floor((lrnN-1)/2), lookAhead = lrnN-lookBehind-1.
 * Values of double parameters are cast to the tensor data type.
 *
 * @param[in,out] normDesc  LRN descriptor to configure.
 * @param[in]     lrnN      Normalization window size (must be in [CUDNN_LRN_MIN_N, CUDNN_LRN_MAX_N]).
 * @param[in]     lrnAlpha  Alpha parameter (must be >= CUDNN_LRN_MIN_K).
 * @param[in]     lrnBeta   Beta parameter (must be >= CUDNN_LRN_MIN_BETA).
 * @param[in]     lrnK      K parameter.
 *
 * @retval CUDNN_STATUS_SUCCESS     The descriptor was set successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @see cudnnGetLRNDescriptor
 */
cudnnStatus_t CUDNNWINAPI
cudnnSetLRNDescriptor(cudnnLRNDescriptor_t normDesc, unsigned lrnN, double lrnAlpha, double lrnBeta, double lrnK);
/**
 * @brief Retrieves the settings of an LRN descriptor.
 *
 * Any of the output pointers can be NULL (the corresponding value will not be returned).
 *
 * @param[in]  normDesc  LRN descriptor to query.
 * @param[out] lrnN      Normalization window size.
 * @param[out] lrnAlpha  Alpha parameter.
 * @param[out] lrnBeta   Beta parameter.
 * @param[out] lrnK      K parameter.
 *
 * @retval CUDNN_STATUS_SUCCESS  The descriptor was queried successfully.
 *
 * @since cuDNN 9.0.0
 * @see cudnnSetLRNDescriptor
 */
cudnnStatus_t CUDNNWINAPI
cudnnGetLRNDescriptor(cudnnLRNDescriptor_t normDesc, unsigned *lrnN, double *lrnAlpha, double *lrnBeta, double *lrnK);

/**
 * @brief Destroys an LRN descriptor.
 *
 * @param[in] lrnDesc  LRN descriptor to destroy.
 *
 * @retval CUDNN_STATUS_SUCCESS  The descriptor was destroyed successfully.
 *
 * @since cuDNN 9.0.0
 * @see cudnnCreateLRNDescriptor
 */
cudnnStatus_t CUDNNWINAPI
cudnnDestroyLRNDescriptor(cudnnLRNDescriptor_t lrnDesc);

/* LRN functions: output = alpha * normalize(x) + beta * old_y */

/**
 * @brief Performs forward LRN cross-channel normalization.
 *
 * Computes y = alpha * normalize(x) + beta * y. Double parameters are cast
 * to the tensor data type.
 *
 * @param[in]     handle    cuDNN library handle.
 * @param[in]     normDesc  LRN descriptor.
 * @param[in]     lrnMode   LRN mode.
 * @param[in]     alpha     Scaling factor for the normalization result.
 * @param[in]     xDesc     Input tensor descriptor.
 * @param[in]     x         Pointer to input tensor data.
 * @param[in]     beta      Scaling factor for the destination tensor.
 * @param[in]     yDesc     Output tensor descriptor.
 * @param[in,out] y         Pointer to output tensor data.
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @see cudnnLRNCrossChannelBackward
 */
cudnnStatus_t CUDNNWINAPI
cudnnLRNCrossChannelForward(cudnnHandle_t handle,
                            cudnnLRNDescriptor_t normDesc,
                            cudnnLRNMode_t lrnMode,
                            const void *alpha,
                            const cudnnTensorDescriptor_t xDesc,
                            const void *x,
                            const void *beta,
                            const cudnnTensorDescriptor_t yDesc,
                            void *y);

/**
 * @brief Selects the divisive normalization mode.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_DIVNORM_PRECOMPUTED_MEANS = 0, /**< Use precomputed means for divisive normalization. @since cuDNN 9.0.0 */
} cudnnDivNormMode_t;

/**
 * @brief Performs forward divisive normalization.
 *
 * Computes y = alpha * normalize(x) + beta * y. If means is NULL, means are
 * assumed to be zero. The xDesc is used for means, temp, and temp2 as well.
 *
 * @param[in]     handle    cuDNN library handle.
 * @param[in]     normDesc  LRN descriptor (shared with LRN functions).
 * @param[in]     mode      Divisive normalization mode.
 * @param[in]     alpha     Scaling factor for the normalization result.
 * @param[in]     xDesc     Input tensor descriptor (also used for means, temp, temp2).
 * @param[in]     x         Pointer to input tensor data.
 * @param[in]     means     Pointer to means tensor data (NULL for zero means).
 * @param[out]    temp      Temporary workspace tensor.
 * @param[out]    temp2     Temporary workspace tensor.
 * @param[in]     beta      Scaling factor for the destination tensor.
 * @param[in]     yDesc     Output tensor descriptor.
 * @param[in,out] y         Pointer to output tensor data.
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @see cudnnDivisiveNormalizationBackward
 */
cudnnStatus_t CUDNNWINAPI
cudnnDivisiveNormalizationForward(cudnnHandle_t handle,
                                  cudnnLRNDescriptor_t normDesc,
                                  cudnnDivNormMode_t mode,
                                  const void *alpha,
                                  const cudnnTensorDescriptor_t xDesc, /* same desc for means, temp, temp2 */
                                  const void *x,
                                  const void *means, /* if NULL, means are assumed to be zero */
                                  void *temp,
                                  void *temp2,
                                  const void *beta,
                                  const cudnnTensorDescriptor_t yDesc,
                                  void *y);

/**
 * @brief Selects the batch normalization mode.
 * @since cuDNN 9.0.0
 */
typedef enum {
    /** @brief Per-activation: bnScale/bnBias dims are 1xCxHxWx.. (normalized over N). @since cuDNN 9.0.0 */
    CUDNN_BATCHNORM_PER_ACTIVATION = 0, /**< Per-activation: bnScale/bnBias shape 1xCxHxW, normalized over N. @since cuDNN 9.0.0 */

    /** @brief Spatial: bnScale/bnBias dims are 1xCx1x1 (normalized over NxHxW). @since cuDNN 9.0.0 */
    CUDNN_BATCHNORM_SPATIAL = 1, /**< Spatial: bnScale/bnBias shape 1xCx1x1, normalized over N+spatial dims. @since cuDNN 9.0.0 */

    /**
     * @brief Spatial persistent: same as SPATIAL but may be faster with limits on value range.
     * @since cuDNN 9.0.0
     */
    CUDNN_BATCHNORM_SPATIAL_PERSISTENT = 2, /**< Like SPATIAL but faster via scaled atomic int reduction. NCHW, CC>=6.0. @since cuDNN 9.0.0 */
} cudnnBatchNormMode_t CUDNN_DEPRECATED;

#define CUDNN_BN_MIN_EPSILON 0.0 /* Minimum epsilon allowed to be used in the Batch Normalization formula */

/**
 * @brief Derives a tensor descriptor for batch normalization parameters.
 *
 * Computes the dimensions for bnScale, bnBias, mean, and variance tensors based
 * on the input tensor descriptor and batch normalization mode. Use this for
 * bnScaleBiasMeanVarDesc and bnScaleBiasDiffDesc parameters.
 *
 * @param[in,out] derivedBnDesc  Tensor descriptor to be derived.
 * @param[in]     xDesc          Input tensor descriptor.
 * @param[in]     mode           Batch normalization mode.
 *
 * @retval CUDNN_STATUS_SUCCESS     The descriptor was derived successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnBatchNormalizationForwardTraining
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnDeriveBNTensorDescriptor(cudnnTensorDescriptor_t derivedBnDesc,
                              const cudnnTensorDescriptor_t xDesc,
                              cudnnBatchNormMode_t mode);

/**
 * @brief Selects the extended batch normalization operation mode.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_BATCHNORM_OPS_BN                = 0, /**< Batch normalization only. @since cuDNN 9.0.0 */
    CUDNN_BATCHNORM_OPS_BN_ACTIVATION     = 1, /**< Batch normalization followed by activation. @since cuDNN 9.0.0 */
    CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION = 2, /**< Batch normalization, element-wise add, then activation. @since cuDNN 9.0.0 */
} cudnnBatchNormOps_t CUDNN_DEPRECATED;

/**
 * @brief Performs batch normalization during inference.
 *
 * Computes y[i] = bnScale[k]*(x[i]-estimatedMean[k])/sqrt(epsilon+estimatedVariance[k]) + bnBias[k],
 * with tensors indexed according to spatial or per-activation mode.
 *
 * @param[in]     handle                  cuDNN library handle.
 * @param[in]     mode                    Batch normalization mode.
 * @param[in]     alpha                   Result blend factor.
 * @param[in]     beta                    Destination layer blend factor.
 * @param[in]     xDesc                   Input tensor descriptor.
 * @param[in]     x                       Pointer to input tensor data (NxCxHxW).
 * @param[in]     yDesc                   Output tensor descriptor.
 * @param[in,out] y                       Pointer to output tensor data (NxCxHxW).
 * @param[in]     bnScaleBiasMeanVarDesc  Descriptor for scale, bias, mean, variance tensors.
 * @param[in]     bnScale                 Pointer to scale (gamma) tensor data.
 * @param[in]     bnBias                  Pointer to bias (beta) tensor data.
 * @param[in]     estimatedMean           Pointer to running mean tensor data.
 * @param[in]     estimatedVariance       Pointer to running variance tensor data.
 * @param[in]     epsilon                 Epsilon value (must be >= CUDNN_BN_MIN_EPSILON).
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnBatchNormalizationForwardTraining, cudnnDeriveBNTensorDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnBatchNormalizationForwardInference(cudnnHandle_t handle,
                                        cudnnBatchNormMode_t mode,
                                        const void *alpha, /* alpha[0] = result blend factor */
                                        const void *beta,  /* beta[0] = dest layer blend factor */
                                        const cudnnTensorDescriptor_t xDesc,
                                        const void *x, /* NxCxHxW */
                                        const cudnnTensorDescriptor_t yDesc,
                                        void *y, /* NxCxHxW */
                                        const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
                                        const void *bnScale,
                                        const void *bnBias,
                                        const void *estimatedMean,
                                        const void *estimatedVariance,
                                        double epsilon);

/**
 * @brief Selects the normalization mode.
 * @since cuDNN 9.0.0
 */
typedef enum {
    /** @brief Per-activation: normScale/normBias dims are 1xCxHxWx.. (normalized over N). @since cuDNN 9.0.0 */
    CUDNN_NORM_PER_ACTIVATION = 0, /**< Norm per activation. @since cuDNN 9.0.0 */

    /** @brief Per-channel: normScale/normBias dims are 1xCx1x1 (normalized over NxHxW). @since cuDNN 9.0.0 */
    CUDNN_NORM_PER_CHANNEL = 1, /**< Norm per channel. @since cuDNN 9.0.0 */
} cudnnNormMode_t CUDNN_DEPRECATED;

/**
 * @brief Selects the normalization algorithm.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_NORM_ALGO_STANDARD = 0, /**< Standard normalization algorithm. @since cuDNN UNPUBLISHED */
    CUDNN_NORM_ALGO_PERSIST  = 1  /**< Persistent normalization (requires compute capability 6.0+). @since cuDNN UNPUBLISHED */
} cudnnNormAlgo_t CUDNN_DEPRECATED;

/**
 * @brief Derives tensor descriptors for normalization parameters.
 *
 * Computes the dimensions for normScale, normBias, mean, and variance tensors based
 * on the input tensor descriptor and normalization mode.
 *
 * @param[in,out] derivedNormScaleBiasDesc  Descriptor to be derived for scale/bias tensors.
 * @param[in,out] derivedNormMeanVarDesc    Descriptor to be derived for mean/variance tensors.
 * @param[in]     xDesc                     Input tensor descriptor.
 * @param[in]     mode                      Normalization mode.
 * @param[in]     groupCnt                  Group count (reserved, should be set to 1).
 *
 * @retval CUDNN_STATUS_SUCCESS     The descriptors were derived successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnNormalizationForwardTraining
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnDeriveNormTensorDescriptor(cudnnTensorDescriptor_t derivedNormScaleBiasDesc,
                                cudnnTensorDescriptor_t derivedNormMeanVarDesc,
                                const cudnnTensorDescriptor_t xDesc,
                                cudnnNormMode_t mode,
                                int groupCnt); /* Place hold for future work, should be set to 1 now*/

/**
 * @brief Selects the extended normalization operation mode.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_NORM_OPS_NORM                = 0, /**< Normalization only. @since cuDNN 9.0.0 */
    CUDNN_NORM_OPS_NORM_ACTIVATION     = 1, /**< Normalization followed by activation. @since cuDNN 9.0.0 */
    CUDNN_NORM_OPS_NORM_ADD_ACTIVATION = 2, /**< Normalization, element-wise add, then activation. @since cuDNN 9.0.0 */
} cudnnNormOps_t CUDNN_DEPRECATED;

/**
 * @brief Performs normalization during inference.
 *
 * Computes y[i] = normScale[k]*(x[i]-estimatedMean[k])/sqrt(epsilon+estimatedVariance[k]) + normBias[k],
 * with tensors indexed according to per-channel or per-activation mode.
 *
 * @param[in]     handle             cuDNN library handle.
 * @param[in]     mode               Normalization mode.
 * @param[in]     normOps            Extended normalization operation mode.
 * @param[in]     algo               Normalization algorithm.
 * @param[in]     alpha              Result blend factor.
 * @param[in]     beta               Destination layer blend factor.
 * @param[in]     xDesc              Input tensor descriptor.
 * @param[in]     x                  Pointer to input tensor data (NxCxHxW).
 * @param[in]     normScaleBiasDesc  Descriptor for normalization scale/bias tensors.
 * @param[in]     normScale          Pointer to normalization scale tensor data.
 * @param[in]     normBias           Pointer to normalization bias tensor data.
 * @param[in]     normMeanVarDesc    Descriptor for mean/variance tensors.
 * @param[in]     estimatedMean      Pointer to running mean tensor data.
 * @param[in]     estimatedVariance  Pointer to running variance tensor data.
 * @param[in]     zDesc              Descriptor for z tensor (used with add operations).
 * @param[in]     z                  Pointer to z tensor data.
 * @param[in]     activationDesc     Activation descriptor (used with activation operations).
 * @param[in]     yDesc              Output tensor descriptor.
 * @param[in,out] y                  Pointer to output tensor data (NxCxHxW).
 * @param[in]     epsilon            Epsilon value (must be >= 0).
 * @param[in]     groupCnt           Group count (reserved, should be set to 1).
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnNormalizationForwardTraining, cudnnDeriveNormTensorDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnNormalizationForwardInference(cudnnHandle_t handle,
                                   cudnnNormMode_t mode,
                                   cudnnNormOps_t normOps,
                                   cudnnNormAlgo_t algo,
                                   const void *alpha, /* alpha[0] = result blend factor */
                                   const void *beta,  /* beta[0] = dest layer blend factor */
                                   const cudnnTensorDescriptor_t xDesc,
                                   const void *x, /* NxCxHxW */
                                   const cudnnTensorDescriptor_t normScaleBiasDesc,
                                   const void *normScale,
                                   const void *normBias,
                                   const cudnnTensorDescriptor_t normMeanVarDesc,
                                   const void *estimatedMean,
                                   const void *estimatedVariance,
                                   const cudnnTensorDescriptor_t zDesc,
                                   const void *z,
                                   cudnnActivationDescriptor_t activationDesc,
                                   const cudnnTensorDescriptor_t yDesc,
                                   void *y, /* NxCxHxW */
                                   double epsilon,
                                   int groupCnt); /* Place hold for future work*/

/* APIs for spatial transformer network*/

/**
 * @brief Selects the spatial sampler type for spatial transformer networks.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_SAMPLER_BILINEAR = 0, /**< Bilinear sampler. @since cuDNN 9.0.0 */
} cudnnSamplerType_t;

/**
 * @brief Creates a spatial transformer descriptor.
 *
 * @param[out] stDesc  Pointer to the newly created spatial transformer descriptor.
 *
 * @retval CUDNN_STATUS_SUCCESS       The descriptor was created successfully.
 * @retval CUDNN_STATUS_ALLOC_FAILED  Memory allocation failed.
 *
 * @since cuDNN 9.0.0
 * @see cudnnDestroySpatialTransformerDescriptor
 */
cudnnStatus_t CUDNNWINAPI
cudnnCreateSpatialTransformerDescriptor(cudnnSpatialTransformerDescriptor_t *stDesc);

/**
 * @brief Configures an N-dimensional spatial transformer descriptor.
 *
 * @param[in,out] stDesc      Spatial transformer descriptor to configure.
 * @param[in]     samplerType Type of sampler to use.
 * @param[in]     dataType    Data type of the tensors.
 * @param[in]     nbDims      Number of dimensions.
 * @param[in]     dimA        Array of dimension sizes (length nbDims).
 *
 * @retval CUDNN_STATUS_SUCCESS     The descriptor was set successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @see cudnnSpatialTfGridGeneratorForward, cudnnSpatialTfSamplerForward
 */
cudnnStatus_t CUDNNWINAPI
cudnnSetSpatialTransformerNdDescriptor(cudnnSpatialTransformerDescriptor_t stDesc,
                                       cudnnSamplerType_t samplerType,
                                       cudnnDataType_t dataType,
                                       const int nbDims,
                                       const int dimA[]);

/**
 * @brief Destroys a spatial transformer descriptor.
 *
 * @param[in] stDesc  Spatial transformer descriptor to destroy.
 *
 * @retval CUDNN_STATUS_SUCCESS  The descriptor was destroyed successfully.
 *
 * @since cuDNN 9.0.0
 * @see cudnnCreateSpatialTransformerDescriptor
 */
cudnnStatus_t CUDNNWINAPI
cudnnDestroySpatialTransformerDescriptor(cudnnSpatialTransformerDescriptor_t stDesc);

/**
 * @brief Generates a sampling grid for a spatial transformer (forward).
 *
 * Generates a grid of sampling coordinates from the affine transformation matrix theta.
 *
 * @param[in]  handle  cuDNN library handle.
 * @param[in]  stDesc  Spatial transformer descriptor.
 * @param[in]  theta   Pointer to affine transformation matrices.
 * @param[out] grid    Pointer to output sampling grid data.
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @see cudnnSpatialTfGridGeneratorBackward
 */
cudnnStatus_t CUDNNWINAPI
cudnnSpatialTfGridGeneratorForward(cudnnHandle_t handle,
                                   const cudnnSpatialTransformerDescriptor_t stDesc,
                                   const void *theta,
                                   void *grid);

/**
 * @brief Performs spatial transformer sampling (forward).
 *
 * Samples the input tensor at the grid coordinates to produce the output tensor.
 *
 * @param[in]     handle  cuDNN library handle.
 * @param[in]     stDesc  Spatial transformer descriptor.
 * @param[in]     alpha   Scaling factor for the sampled result.
 * @param[in]     xDesc   Input tensor descriptor.
 * @param[in]     x       Pointer to input tensor data.
 * @param[in]     grid    Pointer to sampling grid data.
 * @param[in]     beta    Scaling factor for the destination tensor.
 * @param[in]     yDesc   Output tensor descriptor.
 * @param[in,out] y       Pointer to output tensor data.
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @see cudnnSpatialTfSamplerBackward
 */
cudnnStatus_t CUDNNWINAPI
cudnnSpatialTfSamplerForward(cudnnHandle_t handle,
                             cudnnSpatialTransformerDescriptor_t stDesc,
                             const void *alpha,
                             const cudnnTensorDescriptor_t xDesc,
                             const void *x,
                             const void *grid,
                             const void *beta,
                             cudnnTensorDescriptor_t yDesc,
                             void *y);

/** @brief Opaque descriptor for dropout operations. @since cuDNN 9.0.0 */
typedef struct cudnnDropoutStruct *cudnnDropoutDescriptor_t;

/**
 * @brief Creates a dropout descriptor.
 *
 * @param[out] dropoutDesc  Pointer to the newly created dropout descriptor.
 *
 * @retval CUDNN_STATUS_SUCCESS       The descriptor was created successfully.
 * @retval CUDNN_STATUS_ALLOC_FAILED  Memory allocation failed.
 *
 * @since cuDNN 9.0.0
 * @see cudnnDestroyDropoutDescriptor
 */
cudnnStatus_t CUDNNWINAPI
cudnnCreateDropoutDescriptor(cudnnDropoutDescriptor_t *dropoutDesc);

/**
 * @brief Destroys a dropout descriptor.
 *
 * @param[in] dropoutDesc  Dropout descriptor to destroy.
 *
 * @retval CUDNN_STATUS_SUCCESS  The descriptor was destroyed successfully.
 *
 * @since cuDNN 9.0.0
 * @see cudnnCreateDropoutDescriptor
 */
cudnnStatus_t CUDNNWINAPI
cudnnDestroyDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc);

/**
 * @brief Returns the size of the states buffer required for dropout.
 *
 * @param[in]  handle      cuDNN library handle.
 * @param[out] sizeInBytes Size of the required states buffer in bytes.
 *
 * @retval CUDNN_STATUS_SUCCESS  The size was returned successfully.
 *
 * @since cuDNN 9.0.0
 * @see cudnnSetDropoutDescriptor
 */
cudnnStatus_t CUDNNWINAPI
cudnnDropoutGetStatesSize(cudnnHandle_t handle, size_t *sizeInBytes);

/**
 * @brief Returns the size of the reserve space required for dropout forward/backward.
 *
 * @param[in]  xdesc       Input tensor descriptor.
 * @param[out] sizeInBytes Size of the required reserve space in bytes.
 *
 * @retval CUDNN_STATUS_SUCCESS  The size was returned successfully.
 *
 * @since cuDNN 9.0.0
 * @see cudnnDropoutForward, cudnnDropoutBackward
 */
cudnnStatus_t CUDNNWINAPI
cudnnDropoutGetReserveSpaceSize(cudnnTensorDescriptor_t xdesc, size_t *sizeInBytes);

/**
 * @brief Configures a dropout descriptor and initializes random state.
 *
 * @param[in,out] dropoutDesc      Dropout descriptor to configure.
 * @param[in]     handle           cuDNN library handle.
 * @param[in]     dropout          Probability of dropping (0 = no dropout, 1 = all dropped).
 * @param[in,out] states           Pointer to device memory for RNG state storage.
 * @param[in]     stateSizeInBytes Size of the states buffer in bytes.
 * @param[in]     seed             Seed for the random number generator.
 *
 * @retval CUDNN_STATUS_SUCCESS     The descriptor was configured successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @see cudnnGetDropoutDescriptor, cudnnRestoreDropoutDescriptor
 */
cudnnStatus_t CUDNNWINAPI
cudnnSetDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc,
                          cudnnHandle_t handle,
                          float dropout,
                          void *states,
                          size_t stateSizeInBytes,
                          unsigned long long seed);

/**
 * @brief Restores a dropout descriptor to a previously saved state.
 *
 * @param[in,out] dropoutDesc      Dropout descriptor to restore.
 * @param[in]     handle           cuDNN library handle.
 * @param[in]     dropout          Dropout probability.
 * @param[in]     states           Pointer to previously saved RNG state.
 * @param[in]     stateSizeInBytes Size of the states buffer in bytes.
 * @param[in]     seed             Seed used to initialize the original state.
 *
 * @retval CUDNN_STATUS_SUCCESS     The descriptor was restored successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @see cudnnSetDropoutDescriptor
 */
cudnnStatus_t CUDNNWINAPI
cudnnRestoreDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc,
                              cudnnHandle_t handle,
                              float dropout,
                              void *states,
                              size_t stateSizeInBytes,
                              unsigned long long seed);

/**
 * @brief Retrieves the settings of a dropout descriptor.
 *
 * @param[in]  dropoutDesc  Dropout descriptor to query.
 * @param[in]  handle       cuDNN library handle.
 * @param[out] dropout      Dropout probability.
 * @param[out] states       Pointer to RNG state memory.
 * @param[out] seed         Seed used for the RNG.
 *
 * @retval CUDNN_STATUS_SUCCESS  The descriptor was queried successfully.
 *
 * @since cuDNN 9.0.0
 * @see cudnnSetDropoutDescriptor
 */
cudnnStatus_t CUDNNWINAPI
cudnnGetDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc,
                          cudnnHandle_t handle,
                          float *dropout,
                          void **states,
                          unsigned long long *seed);

/**
 * @brief Performs forward dropout.
 *
 * Randomly sets elements to zero based on the dropout probability. The reserve
 * space stores the mask for use in the backward pass.
 *
 * @param[in]     handle                  cuDNN library handle.
 * @param[in]     dropoutDesc             Dropout descriptor.
 * @param[in]     xdesc                   Input tensor descriptor.
 * @param[in]     x                       Pointer to input tensor data.
 * @param[in]     ydesc                   Output tensor descriptor.
 * @param[out]    y                       Pointer to output tensor data.
 * @param[out]    reserveSpace            Pointer to reserve space for the dropout mask.
 * @param[in]     reserveSpaceSizeInBytes Size of reserve space in bytes.
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @see cudnnDropoutBackward, cudnnDropoutGetReserveSpaceSize
 */
cudnnStatus_t CUDNNWINAPI
cudnnDropoutForward(cudnnHandle_t handle,
                    const cudnnDropoutDescriptor_t dropoutDesc,
                    const cudnnTensorDescriptor_t xdesc,
                    const void *x,
                    const cudnnTensorDescriptor_t ydesc,
                    void *y,
                    void *reserveSpace,
                    size_t reserveSpaceSizeInBytes);

/* TODO: move these enums out to the appropriate submodule */

/**
 * @brief Enumerates convolution forward algorithms.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM         = 0, /**< Implicit GEMM: matrix product without forming input matrix. No extra workspace. @since cuDNN 9.0.0 */
    CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM = 1, /**< Implicit GEMM with precomputed indices. Needs workspace for index precomputation. @since cuDNN 9.0.0 */
    CUDNN_CONVOLUTION_FWD_ALGO_GEMM                  = 2, /**< Explicit GEMM: forms input matrix explicitly. Requires significant workspace. @since cuDNN 9.0.0 */
    CUDNN_CONVOLUTION_FWD_ALGO_DIRECT                = 3, /**< Direct convolution without matrix multiplication. @since cuDNN 9.0.0 */
    CUDNN_CONVOLUTION_FWD_ALGO_FFT                   = 4, /**< FFT-based convolution. Requires significant workspace. @since cuDNN 9.0.0 */
    CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING            = 5, /**< FFT with tiled inputs. Significant workspace but less than FFT for large inputs. @since cuDNN 9.0.0 */
    CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD              = 6, /**< Winograd transform. Moderate workspace. Not supported on Hopper+. @since cuDNN 9.0.0 */
    CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED     = 7, /**< Winograd non-fused variant. May require significant workspace. @since cuDNN 9.0.0 */
    CUDNN_CONVOLUTION_FWD_ALGO_COUNT                 = 8 /**< Number of forward convolution algorithms. @since cuDNN 9.0.0 */
} cudnnConvolutionFwdAlgo_t;

/**
 * @brief Enumerates convolution backward filter algorithms.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0                 = 0, /**< Sum of matrix products with atomic adds. Non-deterministic. @since cuDNN 9.0.0 */
    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1                 = 1, /**< Implicit GEMM without forming input matrix. Deterministic. @since cuDNN 9.0.0 */
    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT               = 2, /**< FFT-based. Significant workspace. Deterministic. @since cuDNN 9.0.0 */
    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_3                 = 3, /**< Like ALGO_0 with precomputed indices. Non-deterministic. @since cuDNN 9.0.0 */
    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD          = 4, /**< Winograd transform (not implemented). @since cuDNN 9.0.0 */
    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED = 5, /**< Winograd non-fused. Significant workspace. Deterministic. @since cuDNN 9.0.0 */
    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT_TILING        = 6, /**< FFT with tiling. Significant workspace. Deterministic. @since cuDNN 9.0.0 */
    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_COUNT             = 7 /**< Number of backward filter algorithms. @since cuDNN 9.0.0 */
} cudnnConvolutionBwdFilterAlgo_t;

/**
 * @brief Enumerates convolution backward data algorithms.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_CONVOLUTION_BWD_DATA_ALGO_0                 = 0, /**< Sum of matrix products with atomic adds. Non-deterministic. @since cuDNN 9.0.0 */
    CUDNN_CONVOLUTION_BWD_DATA_ALGO_1                 = 1, /**< Implicit GEMM without forming input matrix. Deterministic. @since cuDNN 9.0.0 */
    CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT               = 2, /**< FFT-based. Significant workspace. Deterministic. @since cuDNN 9.0.0 */
    CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT_TILING        = 3, /**< FFT with tiling. Significant workspace. Deterministic. @since cuDNN 9.0.0 */
    CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD          = 4, /**< Winograd transform. Moderate workspace. Deterministic. Not on Hopper+. @since cuDNN 9.0.0 */
    CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED = 5, /**< Winograd non-fused. Significant workspace. Deterministic. @since cuDNN 9.0.0 */
    CUDNN_CONVOLUTION_BWD_DATA_ALGO_COUNT             = 6 /**< Number of backward data algorithms. @since cuDNN 9.0.0 */
} cudnnConvolutionBwdDataAlgo_t;

/**
 * @brief Enumerates CTC loss computation algorithms.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_CTC_LOSS_ALGO_DETERMINISTIC     = 0, /**< Deterministic CTC loss. @since cuDNN UNPUBLISHED */
    CUDNN_CTC_LOSS_ALGO_NON_DETERMINISTIC = 1  /**< Non-deterministic CTC loss. @since cuDNN UNPUBLISHED */
} cudnnCTCLossAlgo_t;

/**
 * @brief Cross-library version checker for the ops sub-library.
 *
 * This function is implemented differently in each sub-library. Each sub-library
 * checks whether its own version matches that of its dependencies.
 *
 * @retval CUDNN_STATUS_SUCCESS                       The version check passed.
 * @retval CUDNN_STATUS_SUBLIBRARY_VERSION_MISMATCH   The versions are inconsistent.
 *
 * @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnOpsVersionCheck(void);

/**
 * @brief Performs backward softmax computation.
 *
 * Computes the gradient of the softmax function.
 *
 * @param[in]     handle  cuDNN library handle.
 * @param[in]     algo    Softmax algorithm used in the forward pass.
 * @param[in]     mode    Softmax computation scope.
 * @param[in]     alpha   Scaling factor for the result.
 * @param[in]     yDesc   Output tensor descriptor (from forward pass).
 * @param[in]     y       Pointer to output tensor data (from forward pass).
 * @param[in]     dyDesc  Output gradient tensor descriptor.
 * @param[in]     dy      Pointer to output gradient tensor data.
 * @param[in]     beta    Scaling factor for the destination tensor.
 * @param[in]     dxDesc  Input gradient tensor descriptor.
 * @param[in,out] dx      Pointer to input gradient tensor data.
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @see cudnnSoftmaxForward
 */
cudnnStatus_t CUDNNWINAPI
cudnnSoftmaxBackward(cudnnHandle_t handle,
                     cudnnSoftmaxAlgorithm_t algo,
                     cudnnSoftmaxMode_t mode,
                     const void *alpha,
                     const cudnnTensorDescriptor_t yDesc,
                     const void *y,
                     const cudnnTensorDescriptor_t dyDesc,
                     const void *dy,
                     const void *beta,
                     const cudnnTensorDescriptor_t dxDesc,
                     void *dx);

/**
 * @brief Performs backward pooling.
 *
 * Computes the gradient of the pooling operation.
 *
 * @param[in]     handle      cuDNN library handle.
 * @param[in]     poolingDesc Pooling descriptor.
 * @param[in]     alpha       Scaling factor for the result.
 * @param[in]     yDesc       Output tensor descriptor (from forward pass).
 * @param[in]     y           Pointer to output tensor data (from forward pass).
 * @param[in]     dyDesc      Output gradient tensor descriptor.
 * @param[in]     dy          Pointer to output gradient tensor data.
 * @param[in]     xDesc       Input tensor descriptor (from forward pass).
 * @param[in]     x           Pointer to input tensor data (from forward pass).
 * @param[in]     beta        Scaling factor for the destination tensor.
 * @param[in]     dxDesc      Input gradient tensor descriptor.
 * @param[in,out] dx          Pointer to input gradient tensor data.
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnPoolingForward
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnPoolingBackward(cudnnHandle_t handle,
                     const cudnnPoolingDescriptor_t poolingDesc,
                     const void *alpha,
                     const cudnnTensorDescriptor_t yDesc,
                     const void *y,
                     const cudnnTensorDescriptor_t dyDesc,
                     const void *dy,
                     const cudnnTensorDescriptor_t xDesc,
                     const void *x,
                     const void *beta,
                     const cudnnTensorDescriptor_t dxDesc,
                     void *dx);

/**
 * @brief Performs backward activation.
 *
 * Computes the gradient of the activation function.
 *
 * @param[in]     handle          cuDNN library handle.
 * @param[in]     activationDesc  Activation descriptor.
 * @param[in]     alpha           Scaling factor for the result.
 * @param[in]     yDesc           Output tensor descriptor (from forward pass).
 * @param[in]     y               Pointer to output tensor data (from forward pass).
 * @param[in]     dyDesc          Output gradient tensor descriptor.
 * @param[in]     dy              Pointer to output gradient tensor data.
 * @param[in]     xDesc           Input tensor descriptor (from forward pass).
 * @param[in]     x               Pointer to input tensor data (from forward pass).
 * @param[in]     beta            Scaling factor for the destination tensor.
 * @param[in]     dxDesc          Input gradient tensor descriptor.
 * @param[in,out] dx              Pointer to input gradient tensor data.
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnActivationForward
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnActivationBackward(cudnnHandle_t handle,
                        cudnnActivationDescriptor_t activationDesc,
                        const void *alpha,
                        const cudnnTensorDescriptor_t yDesc,
                        const void *y,
                        const cudnnTensorDescriptor_t dyDesc,
                        const void *dy,
                        const cudnnTensorDescriptor_t xDesc,
                        const void *x,
                        const void *beta,
                        const cudnnTensorDescriptor_t dxDesc,
                        void *dx);

/**
 * @brief Performs backward LRN cross-channel normalization.
 *
 * Computes the gradient of the LRN cross-channel normalization. Double
 * parameters are cast to the tensor data type.
 *
 * @param[in]     handle    cuDNN library handle.
 * @param[in]     normDesc  LRN descriptor.
 * @param[in]     lrnMode   LRN mode.
 * @param[in]     alpha     Scaling factor for the result.
 * @param[in]     yDesc     Output tensor descriptor (from forward pass).
 * @param[in]     y         Pointer to output tensor data (from forward pass).
 * @param[in]     dyDesc    Output gradient tensor descriptor.
 * @param[in]     dy        Pointer to output gradient tensor data.
 * @param[in]     xDesc     Input tensor descriptor (from forward pass).
 * @param[in]     x         Pointer to input tensor data (from forward pass).
 * @param[in]     beta      Scaling factor for the destination tensor.
 * @param[in]     dxDesc    Input gradient tensor descriptor.
 * @param[in,out] dx        Pointer to input gradient tensor data.
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @see cudnnLRNCrossChannelForward
 */
cudnnStatus_t CUDNNWINAPI
cudnnLRNCrossChannelBackward(cudnnHandle_t handle,
                             cudnnLRNDescriptor_t normDesc,
                             cudnnLRNMode_t lrnMode,
                             const void *alpha,
                             const cudnnTensorDescriptor_t yDesc,
                             const void *y,
                             const cudnnTensorDescriptor_t dyDesc,
                             const void *dy,
                             const cudnnTensorDescriptor_t xDesc,
                             const void *x,
                             const void *beta,
                             const cudnnTensorDescriptor_t dxDesc,
                             void *dx);

/**
 * @brief Performs backward divisive normalization.
 *
 * Computes the gradients of the divisive normalization operation. If means is NULL,
 * means are assumed to be zero.
 *
 * @param[in]     handle       cuDNN library handle.
 * @param[in]     normDesc     LRN descriptor (shared with LRN functions).
 * @param[in]     mode         Divisive normalization mode.
 * @param[in]     alpha        Scaling factor for the result.
 * @param[in]     xDesc        Input tensor descriptor (also used for means, dy, temp, temp2).
 * @param[in]     x            Pointer to input tensor data.
 * @param[in]     means        Pointer to means tensor data (NULL for zero means).
 * @param[in]     dy           Pointer to output gradient tensor data.
 * @param[out]    temp         Temporary workspace tensor.
 * @param[out]    temp2        Temporary workspace tensor.
 * @param[in]     beta         Scaling factor for the destination tensors.
 * @param[in]     dXdMeansDesc Descriptor for dx and dMeans tensors.
 * @param[in,out] dx           Pointer to input gradient tensor data.
 * @param[in,out] dMeans       Pointer to means gradient tensor data (can be NULL).
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @see cudnnDivisiveNormalizationForward
 */
cudnnStatus_t CUDNNWINAPI
cudnnDivisiveNormalizationBackward(cudnnHandle_t handle,
                                   cudnnLRNDescriptor_t normDesc,
                                   cudnnDivNormMode_t mode,
                                   const void *alpha,
                                   const cudnnTensorDescriptor_t xDesc, /* same desc for x, means, dy, temp, temp2 */
                                   const void *x,
                                   const void *means, /* if NULL, means are assumed to be zero */
                                   const void *dy,
                                   void *temp,
                                   void *temp2,
                                   const void *beta,
                                   const cudnnTensorDescriptor_t dXdMeansDesc, /* same desc for dx, dMeans */
                                   void *dx,                                   /* output x differential */
                                   void *dMeans); /* output means differential, can be NULL */

/**
 * @brief Returns the workspace size for extended batch normalization forward training.
 *
 * @param[in]  handle                  cuDNN library handle.
 * @param[in]  mode                    Batch normalization mode.
 * @param[in]  bnOps                   Extended batch normalization operation.
 * @param[in]  xDesc                   Input tensor descriptor.
 * @param[in]  zDesc                   Z tensor descriptor (for add operations).
 * @param[in]  yDesc                   Output tensor descriptor.
 * @param[in]  bnScaleBiasMeanVarDesc  Descriptor for BN parameter tensors.
 * @param[in]  activationDesc          Activation descriptor.
 * @param[out] sizeInBytes             Required workspace size in bytes.
 *
 * @retval CUDNN_STATUS_SUCCESS  The size was returned successfully.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnBatchNormalizationForwardTrainingEx
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize(cudnnHandle_t handle,
                                                         cudnnBatchNormMode_t mode,
                                                         cudnnBatchNormOps_t bnOps,
                                                         const cudnnTensorDescriptor_t xDesc,
                                                         const cudnnTensorDescriptor_t zDesc,
                                                         const cudnnTensorDescriptor_t yDesc,
                                                         const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
                                                         const cudnnActivationDescriptor_t activationDesc,
                                                         size_t *sizeInBytes);

/**
 * @brief Returns the workspace size for extended batch normalization backward.
 *
 * @param[in]  handle            cuDNN library handle.
 * @param[in]  mode              Batch normalization mode.
 * @param[in]  bnOps             Extended batch normalization operation.
 * @param[in]  xDesc             Input tensor descriptor.
 * @param[in]  yDesc             Output tensor descriptor.
 * @param[in]  dyDesc            Output gradient tensor descriptor.
 * @param[in]  dzDesc            Z gradient tensor descriptor.
 * @param[in]  dxDesc            Input gradient tensor descriptor.
 * @param[in]  dBnScaleBiasDesc  Descriptor for BN parameter gradient tensors.
 * @param[in]  activationDesc    Activation descriptor.
 * @param[out] sizeInBytes       Required workspace size in bytes.
 *
 * @retval CUDNN_STATUS_SUCCESS  The size was returned successfully.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnBatchNormalizationBackwardEx
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetBatchNormalizationBackwardExWorkspaceSize(cudnnHandle_t handle,
                                                  cudnnBatchNormMode_t mode,
                                                  cudnnBatchNormOps_t bnOps,
                                                  const cudnnTensorDescriptor_t xDesc,
                                                  const cudnnTensorDescriptor_t yDesc,
                                                  const cudnnTensorDescriptor_t dyDesc,
                                                  const cudnnTensorDescriptor_t dzDesc,
                                                  const cudnnTensorDescriptor_t dxDesc,
                                                  const cudnnTensorDescriptor_t dBnScaleBiasDesc,
                                                  const cudnnActivationDescriptor_t activationDesc,
                                                  size_t *sizeInBytes);

/**
 * @brief Returns the reserve space size for extended batch normalization training.
 *
 * @param[in]  handle          cuDNN library handle.
 * @param[in]  mode            Batch normalization mode.
 * @param[in]  bnOps           Extended batch normalization operation.
 * @param[in]  activationDesc  Activation descriptor.
 * @param[in]  xDesc           Input tensor descriptor.
 * @param[out] sizeInBytes     Required reserve space size in bytes.
 *
 * @retval CUDNN_STATUS_SUCCESS  The size was returned successfully.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnBatchNormalizationForwardTrainingEx
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetBatchNormalizationTrainingExReserveSpaceSize(cudnnHandle_t handle,
                                                     cudnnBatchNormMode_t mode,
                                                     cudnnBatchNormOps_t bnOps,
                                                     const cudnnActivationDescriptor_t activationDesc,
                                                     const cudnnTensorDescriptor_t xDesc,
                                                     size_t *sizeInBytes);

/**
 * @brief Performs batch normalization forward training.
 *
 * Computes y = BN(x). Also accumulates moving averages of mean and inverse variances.
 *
 * @param[in]     handle                     cuDNN library handle.
 * @param[in]     mode                       Batch normalization mode.
 * @param[in]     alpha                      Result blend factor.
 * @param[in]     beta                       Destination layer blend factor.
 * @param[in]     xDesc                      Input tensor descriptor.
 * @param[in]     x                          Pointer to input tensor data (NxCxHxW).
 * @param[in]     yDesc                      Output tensor descriptor.
 * @param[out]    y                          Pointer to output tensor data (NxCxHxW).
 * @param[in]     bnScaleBiasMeanVarDesc     Descriptor for BN parameter tensors.
 * @param[in]     bnScale                    Pointer to scale (gamma) tensor data.
 * @param[in]     bnBias                     Pointer to bias (beta) tensor data.
 * @param[in]     exponentialAverageFactor   Factor for computing running averages.
 * @param[in,out] resultRunningMean          Running mean (updated with exponential average).
 * @param[in,out] resultRunningVariance      Running variance (updated with exponential average).
 * @param[in]     epsilon                    Epsilon value (must be >= CUDNN_BN_MIN_EPSILON).
 * @param[out]    resultSaveMean             Optionally cached mean for backward pass (NULL if unused).
 * @param[out]    resultSaveInvVariance      Optionally cached inverse variance for backward pass (NULL if unused).
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnBatchNormalizationBackward, cudnnDeriveBNTensorDescriptor
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnBatchNormalizationForwardTraining(
    cudnnHandle_t handle,
    cudnnBatchNormMode_t mode,

    const void *alpha, /* alpha[0] = result blend factor */
    const void *beta,  /* beta[0] = dest layer blend factor */

    const cudnnTensorDescriptor_t xDesc,
    const void *x, /* NxCxHxW */
    const cudnnTensorDescriptor_t yDesc,
    void *y, /* NxCxHxW */

    /* Shared desc for the next 6 tensors in the argument list.
       Data type to be set as follows:
       type = (typeOf(x) == double) ? double : float
       Dimensions for this descriptor depend on normalization mode
       - Spatial Normalization : tensors are expected to have dims 1xCx1x1
        (normalization is performed across NxHxW)
       - Per-Activation Normalization : tensors are expected to have dims of 1xCxHxW
        (normalization is performed across N) */
    const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,

    /* 'Gamma' and 'Beta' respectively in Ioffe and Szegedy's paper's notation */
    const void *bnScale,
    const void *bnBias,

    /* MUST use factor=1 in the very first call of a complete training cycle.
       Use a factor=1/(1+n) at N-th call to the function to get
       Cumulative Moving Average (CMA) behavior
       CMA[n] = (x[1]+...+x[n])/n
       Since CMA[n+1] = (n*CMA[n]+x[n+1])/(n+1) =
       ((n+1)*CMA[n]-CMA[n])/(n+1) + x[n+1]/(n+1) =
       CMA[n]*(1-1/(n+1)) + x[n+1]*1/(n+1) */
    double exponentialAverageFactor,

    /* Used in Training phase only.
       runningMean = newMean*factor + runningMean*(1-factor) */
    void *resultRunningMean,
    /* Output in training mode, input in inference. Is the moving average
       of  variance[x] (factor is applied in the same way as for runningMean) */
    void *resultRunningVariance,

    /* Has to be >= CUDNN_BN_MIN_EPSILON. Should be the same in forward and backward functions. */
    double epsilon,

    /* Optionally save intermediate results from the forward pass here
       - can be reused to speed up backward pass. NULL if unused */
    void *resultSaveMean,
    void *resultSaveInvVariance);

/**
 * @brief Performs extended batch normalization forward training with optional activation.
 *
 * Computes y = relu(BN(x) + z). Also accumulates moving averages of mean and inverse variances.
 * Supports fused batch normalization + activation and batch normalization + add + activation.
 *
 * @param[in]     handle                     cuDNN library handle.
 * @param[in]     mode                       Batch normalization mode.
 * @param[in]     bnOps                      Extended batch normalization operation.
 * @param[in]     alpha                      Result blend factor.
 * @param[in]     beta                       Destination layer blend factor.
 * @param[in]     xDesc                      Input tensor descriptor.
 * @param[in]     xData                      Pointer to input tensor data.
 * @param[in]     zDesc                      Z tensor descriptor (for add operations).
 * @param[in]     zData                      Pointer to z tensor data.
 * @param[in]     yDesc                      Output tensor descriptor.
 * @param[out]    yData                      Pointer to output tensor data.
 * @param[in]     bnScaleBiasMeanVarDesc     Descriptor for BN parameter tensors.
 * @param[in]     bnScale                    Pointer to scale tensor data.
 * @param[in]     bnBias                     Pointer to bias tensor data.
 * @param[in]     exponentialAverageFactor   Factor for computing running averages.
 * @param[in,out] resultRunningMean          Running mean.
 * @param[in,out] resultRunningVariance      Running variance.
 * @param[in]     epsilon                    Epsilon value (must be >= CUDNN_BN_MIN_EPSILON).
 * @param[out]    resultSaveMean             Cached mean for backward pass (NULL if unused).
 * @param[out]    resultSaveInvVariance      Cached inverse variance for backward pass (NULL if unused).
 * @param[in]     activationDesc             Activation descriptor.
 * @param[in,out] workspace                  Pointer to workspace memory.
 * @param[in]     workSpaceSizeInBytes       Size of workspace in bytes.
 * @param[in,out] reserveSpace               Pointer to reserve space memory.
 * @param[in]     reserveSpaceSizeInBytes    Size of reserve space in bytes.
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnBatchNormalizationBackwardEx, cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnBatchNormalizationForwardTrainingEx(
    cudnnHandle_t handle,
    cudnnBatchNormMode_t mode,
    cudnnBatchNormOps_t bnOps,

    const void *alpha, /* alpha[0] = result blend factor */
    const void *beta,  /* beta[0] = dest layer blend factor */

    const cudnnTensorDescriptor_t xDesc,
    const void *xData,
    const cudnnTensorDescriptor_t zDesc,
    const void *zData,
    const cudnnTensorDescriptor_t yDesc,
    void *yData,

    const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
    const void *bnScale,
    const void *bnBias,

    double exponentialAverageFactor,
    void *resultRunningMean,
    void *resultRunningVariance,

    /* Has to be >= CUDNN_BN_MIN_EPSILON. Should be the same in forward and backward functions. */
    double epsilon,

    /* Optionally save intermediate results from the forward pass here
       - can be reused to speed up backward pass. NULL if unused */
    void *resultSaveMean,
    void *resultSaveInvVariance,

    cudnnActivationDescriptor_t activationDesc,
    void *workspace,
    size_t workSpaceSizeInBytes,
    void *reserveSpace,
    size_t reserveSpaceSizeInBytes);

/**
 * @brief Performs backward batch normalization.
 *
 * Computes gradients for x, bnScale, and bnBias.
 *
 * @param[in]     handle              cuDNN library handle.
 * @param[in]     mode                Batch normalization mode.
 * @param[in]     alphaDataDiff       Scaling factor for dx result.
 * @param[in]     betaDataDiff        Scaling factor for dx destination.
 * @param[in]     alphaParamDiff      Scaling factor for parameter gradient results.
 * @param[in]     betaParamDiff       Scaling factor for parameter gradient destinations.
 * @param[in]     xDesc               Input tensor descriptor (same for x, dx, dy).
 * @param[in]     x                   Pointer to input tensor data.
 * @param[in]     dyDesc              Output gradient tensor descriptor.
 * @param[in]     dy                  Pointer to output gradient tensor data.
 * @param[in]     dxDesc              Input gradient tensor descriptor.
 * @param[in,out] dx                  Pointer to input gradient tensor data.
 * @param[in]     dBnScaleBiasDesc    Shared descriptor for parameter gradient tensors.
 * @param[in]     bnScale             Pointer to scale tensor data.
 * @param[out]    dBnScaleResult      Pointer to scale gradient result.
 * @param[out]    dBnBiasResult       Pointer to bias gradient result.
 * @param[in]     epsilon             Same epsilon as forward pass.
 * @param[in]     savedMean           Optionally cached mean from forward pass.
 * @param[in]     savedInvVariance    Optionally cached inverse variance from forward pass.
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnBatchNormalizationForwardTraining
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnBatchNormalizationBackward(cudnnHandle_t handle,
                                cudnnBatchNormMode_t mode,
                                const void *alphaDataDiff,
                                const void *betaDataDiff,
                                const void *alphaParamDiff,
                                const void *betaParamDiff,
                                const cudnnTensorDescriptor_t xDesc, /* same desc for x, dx, dy */
                                const void *x,
                                const cudnnTensorDescriptor_t dyDesc,
                                const void *dy,
                                const cudnnTensorDescriptor_t dxDesc,
                                void *dx,
                                /* Shared tensor desc for the 4 tensors below */
                                const cudnnTensorDescriptor_t dBnScaleBiasDesc,
                                const void *bnScale, /* bnBias doesn't affect backpropagation */
                                /* scale and bias diff are not backpropagated below this layer */
                                void *dBnScaleResult,
                                void *dBnBiasResult,
                                /* Same epsilon as forward pass */
                                double epsilon,

                                /* Optionally cached intermediate results from
                                   forward pass */
                                const void *savedMean,
                                const void *savedInvVariance);

/**
 * @brief Performs extended backward batch normalization with optional activation.
 *
 * Computes gradients for the fused batch normalization + activation operations.
 *
 * @param[in]     handle                  cuDNN library handle.
 * @param[in]     mode                    Batch normalization mode.
 * @param[in]     bnOps                   Extended batch normalization operation.
 * @param[in]     alphaDataDiff           Scaling factor for data gradient results.
 * @param[in]     betaDataDiff            Scaling factor for data gradient destinations.
 * @param[in]     alphaParamDiff          Scaling factor for parameter gradient results.
 * @param[in]     betaParamDiff           Scaling factor for parameter gradient destinations.
 * @param[in]     xDesc                   Input tensor descriptor.
 * @param[in]     xData                   Pointer to input tensor data.
 * @param[in]     yDesc                   Output tensor descriptor.
 * @param[in]     yData                   Pointer to output tensor data.
 * @param[in]     dyDesc                  Output gradient tensor descriptor.
 * @param[in]     dyData                  Pointer to output gradient tensor data.
 * @param[in]     dzDesc                  Z gradient tensor descriptor.
 * @param[in,out] dzData                  Pointer to z gradient tensor data.
 * @param[in]     dxDesc                  Input gradient tensor descriptor.
 * @param[in,out] dxData                  Pointer to input gradient tensor data.
 * @param[in]     dBnScaleBiasDesc        Shared descriptor for parameter gradient tensors.
 * @param[in]     bnScaleData             Pointer to scale tensor data.
 * @param[in]     bnBiasData              Pointer to bias tensor data (needed for activation).
 * @param[out]    dBnScaleData            Pointer to scale gradient result.
 * @param[out]    dBnBiasData             Pointer to bias gradient result.
 * @param[in]     epsilon                 Same epsilon as forward pass.
 * @param[in]     savedMean               Optionally cached mean from forward pass.
 * @param[in]     savedInvVariance        Optionally cached inverse variance from forward pass.
 * @param[in]     activationDesc          Activation descriptor.
 * @param[in,out] workSpace               Pointer to workspace memory.
 * @param[in]     workSpaceSizeInBytes    Size of workspace in bytes.
 * @param[in,out] reserveSpace            Pointer to reserve space memory.
 * @param[in]     reserveSpaceSizeInBytes Size of reserve space in bytes.
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnBatchNormalizationForwardTrainingEx
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnBatchNormalizationBackwardEx(cudnnHandle_t handle,
                                  cudnnBatchNormMode_t mode,
                                  cudnnBatchNormOps_t bnOps,

                                  const void *alphaDataDiff,
                                  const void *betaDataDiff,
                                  const void *alphaParamDiff,
                                  const void *betaParamDiff,
                                  const cudnnTensorDescriptor_t xDesc,
                                  const void *xData,
                                  const cudnnTensorDescriptor_t yDesc,
                                  const void *yData,
                                  const cudnnTensorDescriptor_t dyDesc,
                                  const void *dyData,
                                  const cudnnTensorDescriptor_t dzDesc,
                                  void *dzData,
                                  const cudnnTensorDescriptor_t dxDesc,
                                  void *dxData,

                                  /* Shared tensor desc for the 4 tensors below */
                                  const cudnnTensorDescriptor_t dBnScaleBiasDesc,
                                  const void *bnScaleData,
                                  const void *bnBiasData, /* needed if there is activation */
                                  void *dBnScaleData,
                                  void *dBnBiasData,
                                  double epsilon, /* Same epsilon as forward pass */

                                  /* Optionally cached intermediate results from
                                     forward pass */
                                  const void *savedMean,
                                  const void *savedInvVariance,
                                  cudnnActivationDescriptor_t activationDesc,
                                  void *workSpace,
                                  size_t workSpaceSizeInBytes,
                                  void *reserveSpace,
                                  size_t reserveSpaceSizeInBytes);

/**
 * @brief Returns the workspace size for normalization forward training.
 *
 * @param[in]  handle             cuDNN library handle.
 * @param[in]  mode               Normalization mode.
 * @param[in]  normOps            Extended normalization operation.
 * @param[in]  algo               Normalization algorithm.
 * @param[in]  xDesc              Input tensor descriptor.
 * @param[in]  zDesc              Z tensor descriptor (for add operations).
 * @param[in]  yDesc              Output tensor descriptor.
 * @param[in]  normScaleBiasDesc  Descriptor for normalization scale/bias tensors.
 * @param[in]  activationDesc     Activation descriptor.
 * @param[in]  normMeanVarDesc    Descriptor for mean/variance tensors.
 * @param[out] sizeInBytes        Required workspace size in bytes.
 * @param[in]  groupCnt           Group count (reserved, should be set to 1).
 *
 * @retval CUDNN_STATUS_SUCCESS  The size was returned successfully.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnNormalizationForwardTraining
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetNormalizationForwardTrainingWorkspaceSize(cudnnHandle_t handle,
                                                  cudnnNormMode_t mode,
                                                  cudnnNormOps_t normOps,
                                                  cudnnNormAlgo_t algo,
                                                  const cudnnTensorDescriptor_t xDesc,
                                                  const cudnnTensorDescriptor_t zDesc,
                                                  const cudnnTensorDescriptor_t yDesc,
                                                  const cudnnTensorDescriptor_t normScaleBiasDesc,
                                                  const cudnnActivationDescriptor_t activationDesc,
                                                  const cudnnTensorDescriptor_t normMeanVarDesc,
                                                  size_t *sizeInBytes,
                                                  int groupCnt); /* Place hold for future work, should be set to 1 now*/

/**
 * @brief Returns the workspace size for normalization backward.
 *
 * @param[in]  handle              cuDNN library handle.
 * @param[in]  mode                Normalization mode.
 * @param[in]  normOps             Extended normalization operation.
 * @param[in]  algo                Normalization algorithm.
 * @param[in]  xDesc               Input tensor descriptor.
 * @param[in]  yDesc               Output tensor descriptor.
 * @param[in]  dyDesc              Output gradient tensor descriptor.
 * @param[in]  dzDesc              Z gradient tensor descriptor.
 * @param[in]  dxDesc              Input gradient tensor descriptor.
 * @param[in]  dNormScaleBiasDesc  Descriptor for normalization parameter gradient tensors.
 * @param[in]  activationDesc      Activation descriptor.
 * @param[in]  normMeanVarDesc     Descriptor for mean/variance tensors.
 * @param[out] sizeInBytes         Required workspace size in bytes.
 * @param[in]  groupCnt            Group count (reserved, should be set to 1).
 *
 * @retval CUDNN_STATUS_SUCCESS  The size was returned successfully.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnNormalizationBackward
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetNormalizationBackwardWorkspaceSize(cudnnHandle_t handle,
                                           cudnnNormMode_t mode,
                                           cudnnNormOps_t normOps,
                                           cudnnNormAlgo_t algo,
                                           const cudnnTensorDescriptor_t xDesc,
                                           const cudnnTensorDescriptor_t yDesc,
                                           const cudnnTensorDescriptor_t dyDesc,
                                           const cudnnTensorDescriptor_t dzDesc,
                                           const cudnnTensorDescriptor_t dxDesc,
                                           const cudnnTensorDescriptor_t dNormScaleBiasDesc,
                                           const cudnnActivationDescriptor_t activationDesc,
                                           const cudnnTensorDescriptor_t normMeanVarDesc,
                                           size_t *sizeInBytes,
                                           int groupCnt); /* Place hold for future work, should be set to 1 now*/

/**
 * @brief Returns the reserve space size for normalization training.
 *
 * @param[in]  handle          cuDNN library handle.
 * @param[in]  mode            Normalization mode.
 * @param[in]  normOps         Extended normalization operation.
 * @param[in]  algo            Normalization algorithm.
 * @param[in]  activationDesc  Activation descriptor.
 * @param[in]  xDesc           Input tensor descriptor.
 * @param[out] sizeInBytes     Required reserve space size in bytes.
 * @param[in]  groupCnt        Group count (reserved, should be set to 1).
 *
 * @retval CUDNN_STATUS_SUCCESS  The size was returned successfully.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnNormalizationForwardTraining
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetNormalizationTrainingReserveSpaceSize(cudnnHandle_t handle,
                                              cudnnNormMode_t mode,
                                              cudnnNormOps_t normOps,
                                              cudnnNormAlgo_t algo,
                                              const cudnnActivationDescriptor_t activationDesc,
                                              const cudnnTensorDescriptor_t xDesc,
                                              size_t *sizeInBytes,
                                              int groupCnt); /* Place hold for future work, should be set to 1 now*/

/**
 * @brief Performs normalization forward training with optional activation.
 *
 * Computes y = relu(Norm(x) + z). Also accumulates moving averages of mean
 * and inverse variances.
 *
 * @param[in]     handle                     cuDNN library handle.
 * @param[in]     mode                       Normalization mode.
 * @param[in]     normOps                    Extended normalization operation.
 * @param[in]     algo                       Normalization algorithm.
 * @param[in]     alpha                      Result blend factor.
 * @param[in]     beta                       Destination layer blend factor.
 * @param[in]     xDesc                      Input tensor descriptor.
 * @param[in]     xData                      Pointer to input tensor data.
 * @param[in]     normScaleBiasDesc          Descriptor for normalization scale/bias tensors.
 * @param[in]     normScale                  Pointer to scale tensor data.
 * @param[in]     normBias                   Pointer to bias tensor data.
 * @param[in]     exponentialAverageFactor   Factor for computing running averages.
 * @param[in]     normMeanVarDesc            Descriptor for mean/variance tensors.
 * @param[in,out] resultRunningMean          Running mean.
 * @param[in,out] resultRunningVariance      Running variance.
 * @param[in]     epsilon                    Epsilon value (must be >= 0).
 * @param[out]    resultSaveMean             Cached mean for backward pass (NULL if unused).
 * @param[out]    resultSaveInvVariance      Cached inverse variance for backward pass (NULL if unused).
 * @param[in]     activationDesc             Activation descriptor.
 * @param[in]     zDesc                      Z tensor descriptor (for add operations).
 * @param[in]     zData                      Pointer to z tensor data.
 * @param[in]     yDesc                      Output tensor descriptor.
 * @param[out]    yData                      Pointer to output tensor data.
 * @param[in,out] workspace                  Pointer to workspace memory.
 * @param[in]     workSpaceSizeInBytes       Size of workspace in bytes.
 * @param[in,out] reserveSpace               Pointer to reserve space memory.
 * @param[in]     reserveSpaceSizeInBytes    Size of reserve space in bytes.
 * @param[in]     groupCnt                   Group count (reserved, should be set to 1).
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnNormalizationBackward, cudnnGetNormalizationForwardTrainingWorkspaceSize
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnNormalizationForwardTraining(cudnnHandle_t handle,
                                  cudnnNormMode_t mode,
                                  cudnnNormOps_t normOps,
                                  cudnnNormAlgo_t algo,
                                  const void *alpha, /* alpha[0] = result blend factor */
                                  const void *beta,  /* beta[0] = dest layer blend factor */
                                  const cudnnTensorDescriptor_t xDesc,
                                  const void *xData,
                                  const cudnnTensorDescriptor_t normScaleBiasDesc,
                                  const void *normScale,
                                  const void *normBias,
                                  double exponentialAverageFactor,
                                  const cudnnTensorDescriptor_t normMeanVarDesc,
                                  void *resultRunningMean,
                                  void *resultRunningVariance,
                                  /* Has to be >= 0. Should be the same in forward and backward functions. */
                                  double epsilon,
                                  /* Optionally save intermediate results from the forward pass here
                                     - can be reused to speed up backward pass. NULL if unused */
                                  void *resultSaveMean,
                                  void *resultSaveInvVariance,
                                  cudnnActivationDescriptor_t activationDesc,
                                  const cudnnTensorDescriptor_t zDesc,
                                  const void *zData,
                                  const cudnnTensorDescriptor_t yDesc,
                                  void *yData,
                                  void *workspace,
                                  size_t workSpaceSizeInBytes,
                                  void *reserveSpace,
                                  size_t reserveSpaceSizeInBytes,
                                  int groupCnt); /* Place hold for future work, should be set to 1 now*/

/**
 * @brief Performs backward normalization.
 *
 * Computes gradients for the normalization operation, including optional activation
 * and element-wise add gradients.
 *
 * @param[in]     handle                  cuDNN library handle.
 * @param[in]     mode                    Normalization mode.
 * @param[in]     normOps                 Extended normalization operation.
 * @param[in]     algo                    Normalization algorithm.
 * @param[in]     alphaDataDiff           Scaling factor for data gradient results.
 * @param[in]     betaDataDiff            Scaling factor for data gradient destinations.
 * @param[in]     alphaParamDiff          Scaling factor for parameter gradient results.
 * @param[in]     betaParamDiff           Scaling factor for parameter gradient destinations.
 * @param[in]     xDesc                   Input tensor descriptor.
 * @param[in]     xData                   Pointer to input tensor data.
 * @param[in]     yDesc                   Output tensor descriptor.
 * @param[in]     yData                   Pointer to output tensor data.
 * @param[in]     dyDesc                  Output gradient tensor descriptor.
 * @param[in]     dyData                  Pointer to output gradient tensor data.
 * @param[in]     dzDesc                  Z gradient tensor descriptor.
 * @param[in,out] dzData                  Pointer to z gradient tensor data.
 * @param[in]     dxDesc                  Input gradient tensor descriptor.
 * @param[in,out] dxData                  Pointer to input gradient tensor data.
 * @param[in]     dNormScaleBiasDesc      Shared descriptor for parameter gradient tensors.
 * @param[in]     normScaleData           Pointer to scale tensor data.
 * @param[in]     normBiasData            Pointer to bias tensor data (needed for activation).
 * @param[out]    dNormScaleData          Pointer to scale gradient result.
 * @param[out]    dNormBiasData           Pointer to bias gradient result.
 * @param[in]     epsilon                 Same epsilon as forward pass.
 * @param[in]     normMeanVarDesc         Descriptor for mean/variance tensors.
 * @param[in]     savedMean               Optionally cached mean from forward pass.
 * @param[in]     savedInvVariance        Optionally cached inverse variance from forward pass.
 * @param[in]     activationDesc          Activation descriptor.
 * @param[in,out] workSpace               Pointer to workspace memory.
 * @param[in]     workSpaceSizeInBytes    Size of workspace in bytes.
 * @param[in,out] reserveSpace            Pointer to reserve space memory.
 * @param[in]     reserveSpaceSizeInBytes Size of reserve space in bytes.
 * @param[in]     groupCnt                Group count (reserved, should be set to 1).
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @deprecated Since cuDNN 9.0.0. Use graph API instead.
 * @see cudnnNormalizationForwardTraining
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnNormalizationBackward(cudnnHandle_t handle,
                           cudnnNormMode_t mode,
                           cudnnNormOps_t normOps,
                           cudnnNormAlgo_t algo,
                           const void *alphaDataDiff,
                           const void *betaDataDiff,
                           const void *alphaParamDiff,
                           const void *betaParamDiff,
                           const cudnnTensorDescriptor_t xDesc,
                           const void *xData,
                           const cudnnTensorDescriptor_t yDesc,
                           const void *yData,
                           const cudnnTensorDescriptor_t dyDesc,
                           const void *dyData,
                           const cudnnTensorDescriptor_t dzDesc,
                           void *dzData,
                           const cudnnTensorDescriptor_t dxDesc,
                           void *dxData,
                           /* Shared tensor desc for the 4 tensors below */
                           const cudnnTensorDescriptor_t dNormScaleBiasDesc,
                           const void *normScaleData,
                           const void *normBiasData, /* needed if there is activation */
                           void *dNormScaleData,
                           void *dNormBiasData,
                           double epsilon, /* Same epsilon as forward pass */
                           const cudnnTensorDescriptor_t normMeanVarDesc,
                           /* Optionally cached intermediate results from
                              forward pass */
                           const void *savedMean,
                           const void *savedInvVariance,
                           cudnnActivationDescriptor_t activationDesc,
                           void *workSpace,
                           size_t workSpaceSizeInBytes,
                           void *reserveSpace,
                           size_t reserveSpaceSizeInBytes,
                           int groupCnt); /* Place hold for future work, should be set to 1 now*/

/**
 * @brief Computes the gradient of the spatial transformer grid generator (backward).
 *
 * @param[in]  handle  cuDNN library handle.
 * @param[in]  stDesc  Spatial transformer descriptor.
 * @param[in]  dgrid   Pointer to the grid gradient data.
 * @param[out] dtheta  Pointer to the theta gradient data.
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @see cudnnSpatialTfGridGeneratorForward
 */
cudnnStatus_t CUDNNWINAPI
cudnnSpatialTfGridGeneratorBackward(cudnnHandle_t handle,
                                    const cudnnSpatialTransformerDescriptor_t stDesc,
                                    const void *dgrid,
                                    void *dtheta);

/**
 * @brief Performs spatial transformer sampling backward.
 *
 * Computes the gradients of the spatial transformer sampler.
 *
 * @param[in]     handle     cuDNN library handle.
 * @param[in]     stDesc     Spatial transformer descriptor.
 * @param[in]     alpha      Scaling factor for the dx result.
 * @param[in]     xDesc      Input tensor descriptor.
 * @param[in]     x          Pointer to input tensor data.
 * @param[in]     beta       Scaling factor for the dx destination.
 * @param[in]     dxDesc     Input gradient tensor descriptor.
 * @param[in,out] dx         Pointer to input gradient tensor data.
 * @param[in]     alphaDgrid Scaling factor for the dgrid result.
 * @param[in]     dyDesc     Output gradient tensor descriptor.
 * @param[in]     dy         Pointer to output gradient tensor data.
 * @param[in]     grid       Pointer to sampling grid data.
 * @param[in]     betaDgrid  Scaling factor for the dgrid destination.
 * @param[in,out] dgrid      Pointer to grid gradient tensor data.
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @see cudnnSpatialTfSamplerForward
 */
cudnnStatus_t CUDNNWINAPI
cudnnSpatialTfSamplerBackward(cudnnHandle_t handle,
                              cudnnSpatialTransformerDescriptor_t stDesc,
                              const void *alpha,
                              const cudnnTensorDescriptor_t xDesc,
                              const void *x,
                              const void *beta,
                              const cudnnTensorDescriptor_t dxDesc,
                              void *dx,
                              const void *alphaDgrid,
                              const cudnnTensorDescriptor_t dyDesc,
                              const void *dy,
                              const void *grid,
                              const void *betaDgrid,
                              void *dgrid);

/**
 * @brief Performs backward dropout.
 *
 * Applies the same dropout mask from the forward pass (stored in reserveSpace)
 * to the gradient tensor.
 *
 * @param[in]     handle                  cuDNN library handle.
 * @param[in]     dropoutDesc             Dropout descriptor.
 * @param[in]     dydesc                  Output gradient tensor descriptor.
 * @param[in]     dy                      Pointer to output gradient tensor data.
 * @param[in]     dxdesc                  Input gradient tensor descriptor.
 * @param[out]    dx                      Pointer to input gradient tensor data.
 * @param[in]     reserveSpace            Pointer to reserve space from forward pass.
 * @param[in]     reserveSpaceSizeInBytes Size of reserve space in bytes.
 *
 * @retval CUDNN_STATUS_SUCCESS     The operation completed successfully.
 * @retval CUDNN_STATUS_BAD_PARAM   An invalid parameter was provided.
 *
 * @since cuDNN 9.0.0
 * @see cudnnDropoutForward
 */
cudnnStatus_t CUDNNWINAPI
cudnnDropoutBackward(cudnnHandle_t handle,
                     const cudnnDropoutDescriptor_t dropoutDesc,
                     const cudnnTensorDescriptor_t dydesc,
                     const void *dy,
                     const cudnnTensorDescriptor_t dxdesc,
                     void *dx,
                     void *reserveSpace,
                     size_t reserveSpaceSizeInBytes);

#if defined(__cplusplus)
}
#endif

#endif /* CUDNN_OPS_H_ */