typedef struct TFE_TensorHandle TFE_TensorHandle;

TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_NewTensorHandle(const TF_Tensor* t,
                                                            TF_Status* status);
// Indicates that the caller will not be using `h` any more.
TF_CAPI_EXPORT extern void TFE_DeleteTensorHandle(TFE_TensorHandle* h);
TF_CAPI_EXPORT extern TF_DataType TFE_TensorHandleDataType(TFE_TensorHandle* h);

    TFE_MonitoringCounterCell* cell);

// APIs for Counter without label.
typedef struct TFE_MonitoringCounter0 TFE_MonitoringCounter0;
// Returns a new Counter metric object. The caller should manage lifetime of
// the object. Using duplicate metric name will crash the program with fatal
// error.
TF_CAPI_EXPORT extern TFE_MonitoringCounter0* TFE_MonitoringNewCounter0(

      "Creating while loops is not supported on mobile. File a bug at "
      "https://github.com/tensorflow/tensorflow/issues if this feature is "
      "important to you");
#else
  // If it appears the caller created or modified `params`, don't free resources
  if (!ValidateConstWhileParams(*params, status)) return;
  TF_FinishWhileHelper(params, status, outputs);
  FreeWhileResources(params);

                 TF_Status* out_status) const;

 private:
  // Uses "v2_reader_" to build "var name -> shape" and "var name -> data type"
  // maps; both owned by caller.
  // REQUIRES: "v2_reader_ != nullptr && v2_reader_.status().ok()".
  std::pair<std::unique_ptr<TensorSliceReader::VarToShapeMap>,
            std::unique_ptr<TensorSliceReader::VarToDataTypeMap> >
  BuildV2VarMaps();

  //
  // For example some tensor handles may represent distributed values, in which
  // case placement information is lost when resolving the handle.
  //
  // If false, a caller might implement pretty-printing by resolving and
  // iterating over the resulting tensor. This may still be viable if resolving
  // the handle loses information, but `SummarizeValue` would be more precise.

    string key(v2_reader_->key());
    (*var_to_shape_map)[key] = TensorShape(entry.shape());
    (*var_to_data_type_map)[key] = DataType(entry.dtype());
  }
  // The returned pointers are owned by the caller.
  return std::make_pair(std::move(var_to_shape_map),
                        std::move(var_to_data_type_map));
}

}  // namespace checkpoint

  int last_num_graph_nodes;

  // If true, TF_SessionRun and similar methods will call
  // ExtendSessionGraphHelper before running the graph (this is the default
  // public behavior). Can be set to false if the caller needs to call
  // ExtendSessionGraphHelper manually.
  std::atomic<bool> extend_before_run;
};

struct TF_ImportGraphDefOptions {
  tensorflow::ImportGraphDefOptions opts;

// allocated to size `ninputs`. The caller should build `cond_graph` and
// `body_graph` starting from the inputs, and store the final outputs in
// `cond_output` and `body_outputs`.
//
// If `status` is OK, the caller must call either TF_FinishWhile or
// TF_AbortWhile on the returned TF_WhileParams. If `status` isn't OK, the
// returned TF_WhileParams is not valid, and the caller should not call

  // before `StartExecute` is called again. Using `StartExecute` with `Join`
  // allows the caller to schedule computation on multiple ParallelDevices
  // without sequencing those operations (first call `StartExecute` on each
  // parallel device, then call `Join` on each; even if some of the `Join`s
  // return a bad status the caller must run all of the `Join`s or any future
  // `StartExecute`s will deadlock).
  //

TF_Buffer* ProcessGetKeyValueResult(absl::StatusOr<std::string> value,
                                    TF_Status* status) {
  status->status = value.status();
  if (!value.ok()) {
    return nullptr;
  }
  // Caller is responsible to call `TF_DeleteBuffer` to release the buffer.
  TF_Buffer* result = TF_NewBuffer();
  const std::string& value_str = *value;
  void* data = malloc(value_str.length());