code: def prepare_transformer_layer(
      attribute_name: Text,
      config: Dict[Text, Any],
      num_layers: int,
      units: int,
      drop_rate: float,
      unidirectional: bool,
  ) -> Union[
      TransformerEncoder,
      Callable[
          [
              tf.Tensor,
              Optional[tf.Tensor],
              Optional[Union[tf.Tensor, bool]],
          ],
          Tuple[tf.Tensor, Optional[tf.Tensor]],
      ],
  ]:
      """Creates & returns a transformer encoder, potentially with 0 layers."""
      if num_layers > 0:
          return TransformerEncoder(
              num_layers,
              units,
              config[NUM_HEADS],
              units * 4,
              config[REGULARIZATION_CONSTANT],
              dropout_rate=drop_rate,
              attention_dropout_rate=config[DROP_RATE_ATTENTION],
              density=config[CONNECTION_DENSITY],
              unidirectional=unidirectional,
              use_key_relative_position=config[KEY_RELATIVE_ATTENTION],
              use_value_relative_position=config[
                  VALUE_RELATIVE_ATTENTION
              ],
              max_relative_position=config[MAX_RELATIVE_POSITION],
              name=f"{attribute_name}_encoder",
          )
      # create lambda so that it can be used later without the check
      return lambda x, mask, training: (x, None)
  
imports: import tensorflow
  import numpy
  from typing import Text, List, Dict, Any, Union, Optional, Tuple, Callable
  from rasa.shared.nlu.constants import TEXT
  from rasa.utils.tensorflow.model_data import FeatureSignature
  from rasa.utils.tensorflow.constants import REGULARIZATION_CONSTANT, CONNECTION_DENSITY, NUM_TRANSFORMER_LAYERS, TRANSFORMER_SIZE, NUM_HEADS, UNIDIRECTIONAL_ENCODER, KEY_RELATIVE_ATTENTION, VALUE_RELATIVE_ATTENTION, MAX_RELATIVE_POSITION, MASKED_LM, HIDDEN_LAYERS_SIZES, DROP_RATE, SPARSE_INPUT_DROPOUT, DENSE_INPUT_DROPOUT, DENSE_DIMENSION, CONCAT_DIMENSION, DROP_RATE_ATTENTION, SEQUENCE, SENTENCE
  from rasa.utils.tensorflow import layers
  from rasa.utils.tensorflow.exceptions import TFLayerConfigException
  from rasa.utils.tensorflow.transformer import TransformerEncoder
  from rasa.nlu.constants import DEFAULT_TRANSFORMER_SIZE
label_desc: Use image node_id 2 for calling a global function with example usage: prepare_transformer_layer(attribute_name, config, num_layers, units, drop_rate, unidirectional) and returns: function, TransformerEncoder