tensorflow::ServerDef GetServerDef(const std::string& job_name, int num_tasks) {
  tensorflow::ServerDef server_def;
  server_def.set_protocol("grpc");
  server_def.set_job_name(job_name);
  server_def.set_task_index(0);
  tensorflow::ClusterDef* cluster_def = server_def.mutable_cluster();
  tensorflow::JobDef* job_def = cluster_def->add_job();
  job_def->set_name(job_name);
  for (int i = 0; i < num_tasks; i++) {

    if [ -z "${ARTIFACTS}" ]; then
      return 0
    fi

    PATCH_NAME="check-clean-repo-diff.patch"
    PATCH_OUT="${ARTIFACTS}/${PATCH_NAME}"
    git diff > "${PATCH_OUT}"

    [ -n "${JOB_NAME}" ] && [ -n "${BUILD_ID}" ]
    # shellcheck disable=SC2319
    IN_PROW="$?"

    # Don't persist large diffs (30M+) on CI
    LARGE_FILE="$(find "${ARTIFACTS}" -name "${PATCH_NAME}" -type 'f' -size +30M)"

      - name: The job must cleanup
        if: ${{ always() }}
        run: |
          export JOB_NAME=${{ steps.vars.outputs.sha_short }}
          for mode in $(echo compress-encrypt pools erasure); do
             docker-compose -f ${GITHUB_WORKSPACE}/.github/workflows/mint/minio-${mode}.yaml down || true

  auto* task_iter = static_cast<const tensorflow::CoordinatedTask*>(tasks.data);
  for (size_t i = 0; i < tasks.length; ++i) {
    task_vec[i].set_job_name(task_iter->job_name());
    task_vec[i].set_task_id(task_iter->task_id());
    ++task_iter;
  }
  auto results = coord_agent->GetTaskState(task_vec);
  if (!results.ok()) {
    status->status = results.status();
    return;

}  // namespace

absl::Status GradientRegistry::Register(
    const string& op_name, GradientFunctionFactory gradient_function_factory) {
  auto iter = registry_.find(op_name);
  if (iter != registry_.end()) {
    const string error_msg = "Gradient already exists for op: " + op_name + ".";
    return errors::AlreadyExists(error_msg);
  }
  registry_.insert({op_name, gradient_function_factory});
  return absl::OkStatus();
}

	}{
		// Test case  1-4.
		// Cases with invalid bucket name.
		{bucketName: ".test", objName: "obj", PartID: 1, expectedError: fmt.Errorf("%s", "Bucket name invalid: .test")},
		{bucketName: "------", objName: "obj", PartID: 1, expectedError: fmt.Errorf("%s", "Bucket name invalid: ------")},
		{
			bucketName: "$this-is-not-valid-too", objName: "obj", PartID: 1,
			expectedError: fmt.Errorf("%s", "Bucket name invalid: $this-is-not-valid-too"),

OBJ_COUNT=$(./mc ls myminio1/test-bucket --json | jq '.key' | wc -l)
if [ "${OBJ_COUNT}" -ne 1 ]; then
	echo "BUG: Expected no of objects: 1, Found: ${OBJ_COUNT}"
	exit 1
fi

OBJ_NAME=$(./mc ls myminio1/test-bucket --json | jq '.key' | sed 's/"//g')
if [[ ${OBJ_NAME} != "hosts" ]]; then
	echo "BUG: Expected object: hosts, Found: ${BKT_NAME}"
	exit 1
fi

# Finally kill running processes
pkill minio

  // `TF_AttrBuilderCheckCanRunOnDevice()` call(s) on the same `builder`.
  std::set<std::string> attr_names;
};

TF_AttrBuilder* TF_NewAttrBuilder(const char* op_name) {
  return new TF_AttrBuilder(op_name);
}

void TF_DeleteAttrBuilder(TF_AttrBuilder* builder) { delete builder; }

void TF_AttrBuilderSetType(TF_AttrBuilder* builder, const char* attr_name,

  // least one input of the op is watched and has a trainable dtype.
  // op_name is optional and is used for debugging only.
  void RecordOperation(absl::Span<AbstractTensorHandle* const> inputs,
                       absl::Span<AbstractTensorHandle* const> outputs,
                       GradientFunction* gradient_function,
                       const string& op_name = "");
  // Returns whether any tensor in a list of tensors is being watched and has

    }
    if (raw_device_name) {
      device_name_ = raw_device_name;
    }
    op_type_ = op;
    return absl::OkStatus();
  }
  absl::Status SetOpName(const char* const op_name) override {
    if (op_) {
      return errors::FailedPrecondition(
          "SetOpName called on already built op.");
    }
    if (op_type_.empty()) {
      return errors::FailedPrecondition(