code: def call(
      self,
      input_ids: TFModelInputType | None = None,
      past_key_values: Optional[
          Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]
      ] = None,
      attention_mask: np.ndarray | tf.Tensor | None = None,
      token_type_ids: np.ndarray | tf.Tensor | None = None,
      position_ids: np.ndarray | tf.Tensor | None = None,
      head_mask: np.ndarray | tf.Tensor | None = None,
      inputs_embeds: np.ndarray | tf.Tensor | None = None,
      use_cache: Optional[bool] = None,
      output_attentions: Optional[bool] = None,
      output_hidden_states: Optional[bool] = None,
      return_dict: Optional[bool] = None,
      training: Optional[bool] = False,
  ) -> Union[Tuple, TFBaseModelOutputWithPast]:
      outputs = self.transformer(
          input_ids=input_ids,
          past_key_values=past_key_values,
          attention_mask=attention_mask,
          token_type_ids=token_type_ids,
          position_ids=position_ids,
          head_mask=head_mask,
          inputs_embeds=inputs_embeds,
          use_cache=use_cache,
          output_attentions=output_attentions,
          output_hidden_states=output_hidden_states,
          return_dict=return_dict,
          training=training,
      )
      return outputs
  
imports: from __future__ import annotations
  from typing import Optional, Tuple, Union
  import numpy
  import tensorflow
  from ...modeling_tf_outputs import TFBaseModelOutputWithPast, TFCausalLMOutputWithPast, TFSequenceClassifierOutput
  from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFModelInputType, TFPreTrainedModel, TFSequenceClassificationLoss, get_initializer, keras_serializable, unpack_inputs
  from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from .configuration_ctrl import CTRLConfig
label_desc: Use image node_id 2 for calling the TFCTRLModel obj's underlying member method code with example usage: obj.call(input_ids, past_key_values, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict, training) and returns: outputs