//tensorflow/python/debug:grpc_tensorflow_server.par
//tensorflow/python/feature_column:vocabulary_testdata
//tensorflow/python/util:nest_test_main_lib
//tensorflow/lite/experimental/examples/lstm:rnn_cell
//tensorflow/lite/experimental/examples/lstm:rnn_cell.py
//tensorflow/lite/experimental/examples/lstm:unidirectional_sequence_lstm_test
//tensorflow/lite/experimental/examples/lstm:unidirectional_sequence_lstm_test.py
//tensorflow/lite/python:interpreter

// Atomically increments the value of the cell. The value must be non-negative.
TF_CAPI_EXPORT extern void TFE_MonitoringCounterCellIncrementBy(
    TFE_MonitoringCounterCell* cell, int64_t value);

// Retrieves the current value of the cell.
TF_CAPI_EXPORT extern int64_t TFE_MonitoringCounterCellValue(
    TFE_MonitoringCounterCell* cell);

// APIs for Counter without label.

_current_module.__path__ = [_module_dir] + _current_module.__path__

_current_module.nn.rnn_cell = _KerasLazyLoader(
    globals(),
    submodule="__internal__.legacy.rnn_cell",
    name="rnn_cell",
    mode="v1")
if _tf_uses_legacy_keras:
  _module_dir = _module_util.get_parent_dir_for_name(
      "tf_keras.api._v1.keras.__internal__.legacy.rnn_cell")
else:
  _module_dir = _module_util.get_parent_dir_for_name(

TF_Graph::TF_Graph()
    : graph(tensorflow::OpRegistry::Global()),
      refiner(graph.versions().producer(), graph.op_registry()),
      delete_requested(false),
      parent(nullptr),
      parent_inputs(nullptr) {
  // Tell the shape refiner to also run shape inference on functions.
  refiner.set_function_library_for_shape_inference(&graph.flib_def());
}

TF_Graph* TF_NewGraph() { return new TF_Graph; }

  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TF_DeleteStatus(status);
  auto* cell = TFE_MonitoringGetCellCounter0(counter);
  TFE_MonitoringCounterCellIncrementBy(cell, 1);
  EXPECT_EQ(TFE_MonitoringCounterCellValue(cell), 1);
  auto* collection_registry = monitoring::CollectionRegistry::Default();
  monitoring::CollectionRegistry::CollectMetricsOptions options;

  *result = filename_;
  return OkStatus();
}

Status ModularWritableFile::Tell(int64_t* position) {
  if (ops_->tell == nullptr)
    return errors::Unimplemented(
        tensorflow::strings::StrCat("Tell() not implemented for ", filename_));

  UniquePtrTo_TF_Status plugin_status(TF_NewStatus(), TF_DeleteStatus);
  *position = ops_->tell(file_.get(), plugin_status.get());
  return StatusFromTF_Status(plugin_status.get());
}

      description: If compiling from source
  - type: textarea
    id: what-happened
    attributes:
      label: Current behavior?
      description: Also tell us, what did you expect to happen?
      placeholder: Tell us what you see!
    validations:
      required: true
  - type: textarea
    id: code-to-reproduce
    attributes:
      label: Standalone code to reproduce the issue

learning and neural networks. However, the framework is versatile enough to be
used in other areas as well.

TensorFlow provides stable [Python](https://www.tensorflow.org/api_docs/python)
and [C++](https://www.tensorflow.org/api_docs/cc) APIs, as well as a
non-guaranteed backward compatible API for
[other languages](https://www.tensorflow.org/api_docs).

# Therefore, "tfrun" commands cannot include pipes -- which is
# probably for the better. If a pipe is necessary for something, it is probably
# complex. Write a well-documented script under utilities/ to encapsulate the
# functionality instead.
tfrun() { "$@"; }

# Run all "tfrun" commands under Docker. See setup_docker.sh for details
if [[ "$TFCI_DOCKER_ENABLE" == 1 ]]; then

//tensorflow/python/debug:grpc_tensorflow_server.par
//tensorflow/python/feature_column:vocabulary_testdata
//tensorflow/python/util:nest_test_main_lib
//tensorflow/lite/experimental/examples/lstm:rnn_cell
//tensorflow/lite/experimental/examples/lstm:rnn_cell.py
//tensorflow/lite/experimental/examples/lstm:unidirectional_sequence_lstm_test
//tensorflow/lite/experimental/examples/lstm:unidirectional_sequence_lstm_test.py
//tensorflow/lite/python:interpreter