d="$${d#*external/farmhash_archive/src}"
        d="$${d#*external/$${extname}/}"
        d="$${d#_virtual_includes/*/}"
      fi

      mkdir -p "$@/$${d}"
      cp "$${f}" "$@/$${d}/"
    done
    """,
    tags = ["manual"],
    visibility = ["//visibility:public"],
)

# copybara:comment_begin(oss-only)
copy_file(
    name = "root_init_gen",
    src = select({

        `ImageDataGenerator`. It takes you from a structured directory of images
        to a labeled dataset, in one function call. Note that it doesn't perform
        image data augmentation (which is meant to be done using preprocessing
        layers).
    *   **[`text_dataset_from_directory`](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text_dataset_from_directory)**

  auto collective_executor_handle = context->GetCollectiveExecutorHandle();
  tensorflow::Notification done;
  collective_executor_handle->get()->remote_access()->CheckPeerHealth(
      task, timeout_in_ms, [&done, status](const Status& s) {
        status->status = s;
        done.Notify();
      });
  done.WaitForNotification();
}

TF_ShapeAndTypeList* TF_NewShapeAndTypeList(int num_items) {

      # it, and print that list for debugging. Not really clear if this is
      # helpful since the only examples I've seen are enormous.
      bazel cquery "rdeps(kind(py_test, $(cat $BATS_TEST_TMPDIR/deps)), $dep, 1)"
    done < $BATS_TEST_TMPDIR/missing_deps
    exit 1
  fi
}

# The Python package is not allowed to depend on any CUDA packages.
@test "Pip package doesn't depend on CUDA" {
  bazel cquery \

// the gradients of its inputs. Once we have computed all gradients for a single
// input we can mark this input as done, and this can trigger adding an entry to
// the stack if all outputs of that entry are now done.
//
// When the stack is empty we have gradients for all tensors we're interested
// in.

namespace {

template <typename BackwardFunction, typename TapeTensor>

  """Loop over user prompts for an ENV param until receiving a valid response.

  For the env param var_name, read from the environment or verify user input
  until receiving valid input. When done, set var_name in the environ_cp to its
  new value.

  Args:
    environ_cp: (Dict) copy of the os.environ.
    var_name: (String) string for name of environment variable, e.g. "TF_MYVAR".