permissions:
  contents: read

jobs:
  build:
    if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks
    runs-on: [self-hosted, linux, ARM64]
    strategy:
      fail-fast: false
      matrix:
        pyver: ['3.9', '3.10', '3.11', '3.12']
    steps:
      - name: Stop old running containers (if any)
        shell: bash
        run: |

The Code of Conduct also applies within project spaces and in public spaces whenever an individual is representing TensorFlow or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed or de facto representative at an online or offline event. 


## Conflict Resolution

  else:
    importlib.import_module("keras.src.optimizers")
except (ImportError, AttributeError):
  pass

del importlib

# Delete modules that should be hidden from dir().
# Don't fail if these modules are not available.
# For e.g. this file will be originally placed under tensorflow/_api/v1 which
# does not have "python", "core" directories. Then, it will be copied

  if [[ -s $BATS_TEST_TMPDIR/actual_missing_licenses ]]; then
    echo "Please include the missing licenses for the following packages in $LICENSES_TARGET:"
    cat $BATS_TEST_TMPDIR/actual_missing_licenses
  fi

  # Fail if either of the two "extras" or "missing" lists are present. If so,
  # then the user will see the above error messages.
  [[ ! -s $BATS_TEST_TMPDIR/actual_extra_licenses ]] && [[ ! -s $BATS_TEST_TMPDIR/actual_missing_licenses ]]
}

// LINT.IfChange
// Note: Keep in sync with internal copy of enum in eager/context.h.
typedef enum TFE_ContextDevicePlacementPolicy {
  // Running operations with input tensors on the wrong device will fail.
  TFE_DEVICE_PLACEMENT_EXPLICIT = 0,
  // Copy the tensor to the right device but log a warning.
  TFE_DEVICE_PLACEMENT_WARN = 1,
  // Silently copy the tensor, which has a performance cost since the operation

    string prefix_cmp;
    const char* child_scope_name;
    if (prefix == nullptr) {
      child_scope_name = "gradients";
    } else {
      prefix_cmp = string(prefix) + "/";
      // The operation should fail if the provided name prefix has already been
      // used in this graph
      for (const auto& pair : g->name_map) {
        const string& name = pair.first;

      elem.text = indent_str + '  '
    if not elem.tail or not elem.tail.strip():
      elem.tail = indent_str
    for elem in elem:
      indent_xml(elem, level + 1)
    if not elem.tail or not elem.tail.strip():
      elem.tail = indent_str
  else:
    if level and (not elem.tail or not elem.tail.strip()):
      elem.tail = indent_str


def create_xml_file(result_store_dict: ResultDictType,

    )
    p.text += "\n      probably caused the Kokoro invocation to fail."
  else:
    p.text += (
        "\n      Since there were not three failures, this is probably a flake."
    )
    p.text += (
        "\n      Flakes make this pkg/pip_and_nonpip_tests target show "
        "as failing,"
    )
    p.text += "\n      but do not make the Kokoro invocation fail."

os.makedirs(os.path.dirname(sys.argv[2]), exist_ok=True)

class Variable {
 public:
  // Construct a Variable from a resource-dtype TFE_TensorHandle and an
  // indication of the dtype of the variable's value.
  //
  // Note that creating this resource-dtype handle can fail, so `Create` is a
  // separate static method which returns a status.
  Variable(TFE_TensorHandle* handle, TF_DataType type)
      : handle_(handle), type_(type) {}

  // of the ParallelDevice.
  //
  // Sets a bad status and returns a nullptr if `tensor` is already on the
  // ParallelDevice, or if the individual copies fail.
  std::unique_ptr<ParallelTensor> CopyToParallelDevice(TFE_Context* context,
                                                       TFE_TensorHandle* tensor,