Status TF_TensorToTensor(const TF_Tensor* src, Tensor* dst);

namespace {

static void ExpectHasSubstr(StringPiece s, StringPiece expected) {
  EXPECT_TRUE(absl::StrContains(s, expected))
      << "'" << s << "' does not contain '" << expected << "'";
}

// Returns the GPU device name if there is one (with arbitrary tie breaking if
// there are more than one), or "" otherwise.
string GPUDeviceName(TF_Session* session) {

// which is passed to the functions referenced in the TFE_CustomDevice struct
// `device` (execute, delete_device, etc.). It can for example contain the
// names of wrapped devices.
//
// There are currently no graph semantics implemented for registered custom
// devices, so executing tf.functions which contain operations placed on the
// custom devices will fail.
//
// `device_name` must not name an existing physical or custom device. It must

# targets matching the provided query, which start with // or @ but not
# //tensorflow (so it looks for //third_party, //external, etc.), and then
# gathers the list of all packages (i.e. directories) which contain those
# targets.
license_query() {
 bazel cquery --experimental_cc_shared_library "$1" --keep_going \
  | grep -e "^//" -e "^@" \
  | grep -E -v "^//tensorflow" \
  | sed -e 's|:.*||' \
  | sort -u
}

  """Reset file that contains customized config settings."""
  open(_TF_BAZELRC, 'w').close()


def cleanup_makefile():
  """Delete any leftover BUILD files from the Makefile build.

  These files could interfere with Bazel parsing.
  """
  makefile_download_dir = os.path.join(_TF_WORKSPACE_ROOT, 'tensorflow',
                                       'contrib', 'makefile', 'downloads')

                            const char** containers, int ncontainers,
                            TF_Status* status) {
  std::vector<string> container_names(ncontainers);
  for (int i = 0; i < ncontainers; ++i) {
    container_names[i] = containers[i];
  }

  status->status = Reset(opt->options, container_names);
}

extern "C" {

void TF_Reset(const TF_SessionOptions* opt, const char** containers,

    *   Deprecate `tf.contrib.learn`. Please check contrib/learn/README.md for
        instructions on how to convert existing code.
    *   `tf.contrib.data`
    *   Remove deprecated `tf.contrib.data.Dataset`, `tf.contrib.data.Iterator`,
        `tf.contrib.data.FixedLengthRecordDataset`,
        `tf.contrib.data.TextLineDataset`, and `tf.contrib.data.TFRecordDataset`
        classes.

             : absl::OkStatus();
}

// Converts `ninputs` and `inputs` into `inputs_tensors` and `input_nodes` and
// does various checks while doing so. `input_nodes` will contain the same
// information as input_tensors just in a different structure to make
// following processing easier. TODO(iga): Simplify this nested structure.
Status ProcessInputs(

          if [[ $running_containers == "" ]]; then
            echo "No running containers";
          else
            echo "Running container(s) found" && \
            docker stop $running_containers;
          fi
          docker container prune -f
      - name: Clean repository
        shell: bash

limitations under the License.
==============================================================================*/

#include "tensorflow/c/eager/c_api_unified_experimental.h"

#include <vector>

#include "absl/container/flat_hash_map.h"
#include "absl/strings/str_cat.h"
#include "tensorflow/c/eager/c_api_unified_experimental_internal.h"
#include "tensorflow/c/tf_datatype.h"
#include "tensorflow/c/tf_status.h"

### Typical Pull Request Workflow -

**1. New PR**

- As a contributor, you submit a New PR on GitHub.
- We inspect every incoming PR and add certain labels to the PR such as `size:`,
  `comp:` etc.  At this stage we check if the PR is valid and meets certain
  quality requirements. For example, we check if the CLA is signed, PR has
  sufficient description, if applicable unit tests are added, if it is a