delete reinterpret_cast<DataType*>(data);
            },
            nullptr),
        TF_DeleteTensor);
    // TODO(allenl): Here and when executing regular operations, we could hold
    // on to one TFE_Op per device and just call TFE_ResetOp to avoid parsing
    // device names repeatedly.
    std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> const_op(

// The elements of `dims` will point to addresses in `storage` which must be
// large enough to hold at least `storage_size` int64_ts.  Ideally, `num_shapes`
// would be set to TF_AttrMetadata.list_size and `storage_size` would be set to
// TF_AttrMetadata.total_size from TF_OperationGetAttrMetadata(oper,
// attr_name).
//
// Fails if storage_size is insufficient to hold the requested shapes.
TF_CAPI_EXPORT extern void TF_OperationGetAttrShapeList(

 * this will calculate dy/dw numerically.
 *
 * `use_function` indicates whether to use graph mode(true) or eager(false).
 *
 * `numerical_grad` is the pointer to the AbstractTensorHandle* which will
 * hold the numerical gradient data at the end of the function.
 */
Status CalcNumericalGrad(AbstractContext* ctx, Model forward,
                         absl::Span<AbstractTensorHandle* const> inputs,

// least `storage_size` bytes in length.  `num_values` and `storage` can be
// obtained from TF_GetNodesToPreserveSize
//
// Fails if storage_size is too small to hold the requested number of strings.
TF_CAPI_EXPORT extern void TF_GetNodesToPreserveList(
    const TF_GrapplerItem* item, char** values, size_t* lengths, int num_values,
    void* storage, size_t storage_size, TF_Status* status);

                       const ComputeFunc& compute_func, TF_Status* status) {
    if (max_age_ == 0) {
      return compute_func(key, value, status);
    }

    // Note: we hold onto mu_ for the rest of this function. In practice, this
    // is okay, as stat requests are typically fast, and concurrent requests are
    // often for the same file. Future work can split this up into one lock per

#include <memory>

#include "tensorflow/c/experimental/filesystem/filesystem_interface.h"
#include "tensorflow/core/platform/file_system.h"

/// This file builds classes needed to hold a filesystem implementation in the
/// modular world. Once all TensorFlow filesystems are converted to use the
/// plugin based approach, this file will replace the one in core/platform and

 public:
  using GradientTape<AbstractTensorHandle, GradientFunction,
                     TapeTensor>::GradientTape;
  // Returns whether the tape is persistent, i.e., whether the tape will hold
  // onto its internal state after a call to `ComputeGradient`.
  using GradientTape<AbstractTensorHandle, GradientFunction,
                     TapeTensor>::IsPersistent;

    TF_CoordinationServiceAgent* agent, TF_Status* status);

// Obtains key-value from coordination service agent. The returned `TF_Buffer`
// is a newly allocated buffer to hold the string key-value, and caller is
// responsible for managing the lifetime. If error, `status` will be set and a
// nullptr will be returned.
TF_CAPI_EXPORT extern TF_Buffer* TF_CoordinationServiceGetKeyValue(

///   3. Versioning metadata;
///   4. Plugin registration API and the DSO entry point.

#ifdef __cplusplus
extern "C" {
#endif  // __cplusplus

/// SECTION 1. Opaque data structures to hold plugin specific data
/// ----------------------------------------------------------------------------
///
/// The following data structures incorporate a `void*` that is opaque to