<< " at position " << pos << " with data size " << data.size();
      TF_SetStatus(status, TF_OUT_OF_RANGE, std::move(os).str().c_str());
      return total_bytes_transferred;
    }
    auto begin = data.begin();
    if (offset > pos) {
      // The block begins before the slice we're reading.
      begin += offset - pos;
    }
    auto end = data.end();
    if (pos + data.size() > offset + n) {

  // Copy Tensor data into an array.
  float result_data[4] = {0};
  memcpy(&result_data[0], TF_TensorData(result_tensor),
         TF_TensorByteSize(result_tensor));

  int data_len = 4;  // length of result_data
  for (int i = 0; i < data_len; i++) {
    EXPECT_EQ(result_data[i], 0);
  }

  TF_DeleteTensor(result_tensor);

  TFE_MonitoringStringGaugeCellSet(cell2, "str");
  auto* buf = new TF_Buffer;
  TFE_MonitoringStringGaugeCellValue(cell2, buf);
  string data(static_cast<const char*>(buf->data), buf->length);
  TF_DeleteBuffer(buf);
  EXPECT_EQ(data, "str");
  TFE_MonitoringDeleteStringGauge2(gauge2);
  TF_DeleteStatus(status);
}

TEST(CAPI, MonitoringSampler0) {
  TF_Status* status = TF_NewStatus();

  return OkStatus();
}

Status ModularWritableFile::Append(StringPiece data) {
  if (ops_->append == nullptr)
    return errors::Unimplemented(tensorflow::strings::StrCat(
        "Append() not implemented for ", filename_));

  UniquePtrTo_TF_Status plugin_status(TF_NewStatus(), TF_DeleteStatus);
  ops_->append(file_.get(), data.data(), data.size(), plugin_status.get());
  return StatusFromTF_Status(plugin_status.get());
}

                 std::vector<char>* out) {
  out->clear();
  out->resize(n, 0);
  TF_Status status;
  auto bytes_transferred =
      cache->Read(filename, offset, n, out->data(), &status);
  if (bytes_transferred >= 0) {
    EXPECT_LE(bytes_transferred, n);
    out->resize(bytes_transferred, n);
  }
  return status.status;
}

TEST(RamFileBlockCacheTest, IsCacheEnabled) {

      or a Contribution incorporated within the Work constitutes direct
      or contributory patent infringement, then any patent licenses
      granted to You under this License for that Work shall terminate
      as of the date such litigation is filed.

   4. Redistribution. You may reproduce and distribute copies of the
      Work or Derivative Works thereof in any medium, with or without

  os.makedirs(os.path.dirname(output_path), exist_ok=True)
  failure_count = 0
  error_count = 0

  date_time = datetime.datetime
  attrib = {'name': 'Bazel Invocations', 'time': '0.0',
            'timestamp': date_time.isoformat(date_time.utcnow())}
  testsuites = ElemTree.Element('testsuites')
  testsuite = ElemTree.SubElement(testsuites, 'testsuite')

    python tf_doctest.py --file=<file_path>
    ```

    This will run it using your installed version of TensorFlow. To be sure
    you're running the same code that you're testing:

    *   Use an up to date [tf-nightly](https://pypi.org/project/tf-nightly/)
        `pip install -U tf-nightly`
    *   Rebase your pull request onto a recent pull from
        [TensorFlow's](https://github.com/tensorflow/tensorflow) master branch.

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).

Keep up-to-date with release announcements and security updates by subscribing
to
[******@****.***](https://groups.google.com/a/tensorflow.org/forum/#!forum/announce).
See all the [mailing lists](https://www.tensorflow.org/community/forums).

// Returns the number of elements in dimension `dim_index`.
// Tensor representation on device can be transposed from its representation
// on host. The data contained in dimension `dim_index` on device
// can correspond to the data contained in another dimension in on-host
// representation. The dimensions are indexed using the standard TensorFlow
// major-to-minor order (slowest varying dimension first),