typedef struct TFE_TensorDebugInfo TFE_TensorDebugInfo;

// Retrieves TFE_TensorDebugInfo for `handle`.
// If TFE_TensorHandleTensorDebugInfo succeeds, `status` is set to OK and caller
// is responsible for deleting returned TFE_TensorDebugInfo.
// If TFE_TensorHandleTensorDebugInfo fails, `status` is set to appropriate
// error and nullptr is returned. This function can block till the operation
// that produces `handle` has completed.

// Deletes the specified directory and all subdirectories and files underneath
// it. This is accomplished by traversing the directory tree rooted at dirname
// and deleting entries as they are encountered.
//
// If dirname itself is not readable or does not exist, *undeleted_dir_count is
// set to 1, *undeleted_file_count is set to 0 and an appropriate status (e.g.
// TF_NOT_FOUND) is returned.

// * size_t is used to represent byte sizes of objects that are
//   materialized in the address space of the calling process.
// * int is used as an index into arrays.
// * Deletion functions are safe to call on nullptr.
//
// Questions left to address:
// * Might at some point need a way for callers to provide their own Env.
// * Maybe add TF_TensorShape that encapsulates dimension info.
//

  int value = 0;
  EXPECT_TRUE(cache.Lookup("a", &value));
  EXPECT_EQ(value, 1);

  // Delete the entry.
  EXPECT_TRUE(cache.Delete("a"));
  EXPECT_FALSE(cache.Lookup("a", &value));

  // Try deleting the entry again.
  EXPECT_FALSE(cache.Delete("a"));
  EXPECT_FALSE(cache.Lookup("a", &value));
}

}  // namespace

  std::vector<TapeTensor> output_tensor_info;
  std::vector<int64_t> input_tensor_id;

  // TODO(apassos) consider narrowing down this interface.
  BackwardFunction* backward_function;

  // Should be called before deleting the backward function. TODO(apassos) use
  // unique_ptrs to ensure this happens.
  std::function<void(BackwardFunction*)> backward_function_deleter;
};

        << dirpath;

    TF_DeleteFile(filepath.c_str(), s);
    ASSERT_TF_OK(s) << "TF_DeleteFile failed for " << filepath << ": "
                    << TF_Message(s);

    // Now deleting the directory should work.
    TF_DeleteDir(dirpath.c_str(), s);
    ASSERT_TF_OK(s) << "TF_DeleteDir failed for " << dirpath << ": "
                    << TF_Message(s);

    TF_DeleteStatus(s);
    break;
  }

#define TP_OPTIMIZER_CONFIGS_STRUCT_SIZE \
  TF_OFFSET_OF_END(TP_OptimizerConfigs, scoped_allocator_optimization)

// Struct for Optimizer. Plugin authors must provide an optimize function.
// Creation and deletion functions are optional.
typedef struct TP_Optimizer {
  size_t struct_size;
  void* ext;  // reserved for future use

  // [Optional]
  // Create function for optimizer.
  void* (*create_func)();

  /// This is accomplished by traversing directory tree rooted at `path` and
  /// deleting entries as they are encountered, from leaves to root. Each plugin
  /// is free to choose a different approach which obtains similar results.
  ///
  /// On successful deletion, `status` must be `TF_OK` and `*undeleted_files`
  /// and `*undeleted_dirs` must be 0. On unsuccessful deletion, `status` must

  // a trainable dtype.
  bool ShouldRecord(
      absl::Span<const AbstractTensorHandle* const> tensors) const;
  // Unwatches this tensor on the tape. Mainly used for cleanup when deleting
  // eager tensors.
  void DeleteTrace(const AbstractTensorHandle*);

  // Consumes the internal state of the tape (so cannot be called more than

    *   Modify custom export strategy to account for multidimensional sparse
        float splits.
    *   `Conv2D`, `Conv2DBackpropInput`, `Conv2DBackpropFilter` now supports
        arbitrary dilations with GPU and cuDNNv6 support.
    *   `Estimator` now supports `Dataset`: `input_fn` can return a `Dataset`
        instead of `Tensor`s.
    *   Add `RevBlock`, a memory-efficient implementation of reversible residual