name: TensorFlow Issue Template
description: Use this template to report TensorFlow-related issues
body:
  - type: dropdown
    id: issue-type
    attributes:
      label: Issue type
      description: What type of issue would you like to report?
      multiple: false
      options:
        - Bug
        - Build/Install
        - Performance
        - Support
        - Feature Request
        - Documentation Feature Request

only a placeholder to enable test cases to run. The TensorFlow build replaces
this file with a file generated from [`api_template.__init__.py`](https://www.github.com/tensorflow/tensorflow/blob/master/tensorflow/api_template.__init__.py)
"""
# pylint: disable=g-bad-import-order,protected-access,g-import-not-at-top

import distutils as _distutils
import importlib
import inspect as _inspect

If you depend on functionality not listed there, please file an issue.
"""
contrib = _LazyLoader("contrib", globals(), "tensorflow.contrib",
                      _CONTRIB_WARNING)
# The templated code that replaces the placeholder above sometimes
# sets the __all__ variable. If it does, we have to be sure to add
# "contrib".
if "__all__" in vars():
  vars()["__all__"].append("contrib")

#include "tensorflow/core/lib/monitoring/sampler.h"
#include "tensorflow/core/platform/types.h"

struct TFE_MonitoringCounterCell {
  tensorflow::monitoring::CounterCell cell;
};

template <int NumLabels>
struct TFE_MonitoringCounter {
  template <typename... LabelDesc>
  TFE_MonitoringCounter(const char* name, const char* description,
                        LabelDesc&&... label) {

namespace tf_gcs_filesystem {

// A move-only RAII object that calls a stored cleanup functor when
// destroyed. Cleanup<F> is the return type of gtl::MakeCleanup(F).
template <typename F>
class Cleanup {
 public:
  Cleanup() : released_(true), f_() {}

  template <typename G>
  explicit Cleanup(G&& f)          // NOLINT
      : f_(std::forward<G>(f)) {}  // NOLINT(build/c++11)

  Cleanup(Cleanup&& src)  // NOLINT

// Helper to un-pack `num_replicas` TFE_TensorHandles from one parallel handle.
template <std::size_t num_replicas>
void ExtractPerDeviceValues(
    TFE_Context* context, TFE_TensorHandle* input,
    std::array<TensorHandlePtr, num_replicas>* components, TF_Status* status);

// Helper to pack `num_replicas` TFE_TensorHandles into one parallel handle.
template <std::size_t num_replicas>
TensorHandlePtr CreatePerDeviceValues(

---
name: TensorFlow Lite Op Request
about: Use this template for reporting Lite ops you are using or missing
labels: 'comp:lite'

---

**System information**
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- TensorFlow installed from (source or binary):
- TensorFlow version (or github SHA if from source):


**Provide the text output from tflite_convert**

```
# Copy and paste here
```

TFE_TensorHandle* TestTensorHandleWithDimsInt(TFE_Context* ctx, int data[],
                                              int64_t dims[], int num_dims);

// Return a tensor handle with given type, values and dimensions.
template <class T, TF_DataType datatype>
TFE_TensorHandle* TestTensorHandleWithDims(TFE_Context* ctx, const T* data,
                                           const int64_t* dims, int num_dims) {
  TF_Status* status = TF_NewStatus();

                                                       TF_Status* status) const;

  // Construct a parallel tensor consisting of the scalar values from `values`.
  template <typename DataType>
  std::unique_ptr<ParallelTensor> ScalarsFromSequence(
      absl::Span<const DataType> values, TFE_Context* context,
      TF_Status* status) const;

  std::vector<const char*> raw_strs;
  std::vector<size_t> lengths;
  raw_strs.reserve(values.size());
  lengths.reserve(values.size());
  for (const auto& s : values) {
    raw_strs.emplace_back(s.data());
    lengths.emplace_back(s.size());
  }
  return SetAttrStringList(attr_name,
                           reinterpret_cast<const void**>(raw_strs.data()),
                           lengths.data(), values.size());
}