// The following input string length is large enough to make sure that
    // copy to tstring in large mode.
    std::string source =
        "This is the " + std::to_string(i + 1) + "th. data element\n";
    TF_TString_Copy(&data[i], source.c_str(), source.length());
  }

  TF_DeleteTensor(t);
}

}  // namespace
}  // namespace tensorflow

// TODO(josh11b): Test:

  shapes.resize(num_shapes);
  for (int i = 0; i < num_shapes; ++i) {
    if (proto_lens[i] > std::numeric_limits<int>::max()) {
      status->status = InvalidArgument(
          "length of element ", i, " in the list (", proto_lens[i],
          " bytes) is too large to be parsed by the protocol buffer library");
      return;
    }
    if (!shapes[i].ParseFromArray(protos[i], static_cast<int>(proto_lens[i]))) {

        `tf.data.Dataset.bucket_by_sequence_length` and deprecating the
        experimental endpoint.
    *   Promoting `tf.data.experimental.get_single_element` API to
        `tf.data.Dataset.get_single_element` and deprecating the experimental
        endpoint.
    *   Promoting `tf.data.experimental.group_by_window` API to
        `tf.data.Dataset.group_by_window` and deprecating the experimental

  // once) and produces the gradient of the target tensors with respect to the
  // source tensors. The output gradients are used if not empty and not
  // null. The result is populated with one tensor per target element.
  // When running backward functions, builds zeros-like tensors for
  // incoming grads which are nullptrs, unless `build_default_zeros_grads`
  // is set to false.
  Status ComputeGradient(