/// Look up the entry with key `key` and copy it to `value` if found. If not
  /// found, call `compute_func`. If `compute_func` set `status` to `TF_OK`,
  /// store a copy of the output parameter in the cache, and another copy in
  /// `value`.
  void LookupOrCompute(const std::string& key, T* value,
                       const ComputeFunc& compute_func, TF_Status* status) {
    if (max_age_ == 0) {
      return compute_func(key, value, status);

                          ImmediateExecutionTensorHandle* input) = 0;

  virtual ImmediateExecutionContext* GetContext() const = 0;

  // Following two methods are used to support custom device.
  // Return true if the inputs contain custom device tensor handle. It means
  // that the argument need to be handled by a custom device.
  virtual bool HasCustomDeviceInput() const = 0;

  virtual const tensorflow::OpDef* OpDef() const = 0;

namespace tensorflow {
namespace gradients {

/* Returns numerical grad inside `dtheta_approx` given `forward` model and
 * parameter specified by `input_index`.
 *
 * I.e. if y = <output of the forward model> and w = inputs[input_index],
 * this will calculate dy/dw numerically.
 *
 * `use_function` indicates whether to use graph mode(true) or eager(false).
 *

// Append a tensor at the end of the output list, growing its size by one.
void TF_OutputListPushBack(TF_OutputList* o, TF_AbstractTensor* tensor,
                           TF_Status*);

// TF_ExecuteOperation will, if in eager mode, execute, if in graph mode, maybe
// capture some inputs and then add a node in the graph. The output tensors are
// returned through the provided TF_OutputList.
// Any active tape will observe the effects of this execution.

// 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.
//
// If dirname and all its descendants were successfully deleted, TF_OK is
// returned and both error counters are set to zero.
//

#include <sys/stat.h>

namespace tf_posix_filesystem {

// Transfers up to `size` bytes from `dst_fd` to `src_fd`.
//
// This method uses `sendfile` if available (i.e., linux 2.6.33 or later) or an
// intermediate buffer if not.
//
// Returns number of bytes transferred or -1 on failure.
int CopyFileContents(int dst_fd, int src_fd, off_t size);

}  // namespace tf_posix_filesystem

      TFE_NewOp(context, "TPUReplicatedInput", status), TFE_DeleteOp);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_OpSetAttrInt(op.get(), "N", num_replicas);
  for (int i = 0; i < num_replicas; ++i) {
    TFE_OpAddInput(op.get(), components[i], status);
    if (TF_GetCode(status) != TF_OK) return nullptr;
  }
  TFE_OpSetDevice(op.get(), device, status);
  if (TF_GetCode(status) != TF_OK) return nullptr;

  virtual Status RemoveFunction(const string& func) = 0;

 private:
  const AbstractContextKind kind_;
};

namespace internal {
struct AbstractContextDeleter {
  void operator()(AbstractContext* p) const {
    if (p != nullptr) {
      p->Release();
    }
  }
};
}  // namespace internal

using AbstractContextPtr =
    std::unique_ptr<AbstractContext, internal::AbstractContextDeleter>;

}  // namespace tensorflow

// Allocate a parallel device named `device_name` which forwards operations to
// `underlying_devices`, maintaining "parallel tensors" with components placed
// on each underlying device.
//
// For example if `device_name` is
//   "/job:localhost/replica:0/task:0/device:CUSTOM:0"
// and `underlying_devices` is
//   {"/job:localhost/replica:0/task:0/device:GPU:0",
//    "/job:localhost/replica:0/task:0/device:GPU:1"}

TFE_TensorHandle* TestAxisTensorHandle(TFE_Context* ctx);

// Return an op taking minimum of `input` long `axis` dimension.
TFE_Op* MinOp(TFE_Context* ctx, TFE_TensorHandle* input,
              TFE_TensorHandle* axis);

// If there is a device of type `device_type`, returns true
// and sets 'device_name' accordingly.
// `device_type` must be either "GPU" or "TPU".
bool GetDeviceName(TFE_Context* ctx, tensorflow::string* device_name,