code: def call(
      self,
      input_ids: TFModelInputType | None = None,
      attention_mask: np.ndarray | tf.Tensor | None = None,
      head_mask: np.ndarray | tf.Tensor | None = None,
      past_key_values: Optional[
          Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]
      ] = 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,
      **kwargs,
  ) -> Union[TFBaseModelOutputWithPast, Tuple[tf.Tensor]]:
      output_attentions = (
          output_attentions
          if output_attentions is not None
          else self.config.output_attentions
      )
      output_hidden_states = (
          output_hidden_states
          if output_hidden_states is not None
          else self.config.output_hidden_states
      )
      use_cache = (
          use_cache if use_cache is not None else self.config.use_cache
      )
      return_dict = (
          return_dict
          if return_dict is not None
          else self.config.use_return_dict
      )
  
      outputs = self.decoder(
          input_ids,
          attention_mask=attention_mask,
          head_mask=head_mask,
          past_key_values=past_key_values,
          inputs_embeds=inputs_embeds,
          use_cache=use_cache,
          output_attentions=output_attentions,
          output_hidden_states=output_hidden_states,
          return_dict=return_dict,
          training=training,
      )
  
      if not return_dict:
          return outputs
  
      return TFBaseModelOutputWithPast(
          last_hidden_state=outputs.last_hidden_state,
          past_key_values=outputs.past_key_values,
          hidden_states=outputs.hidden_states,
          attentions=outputs.attentions,
      )
  
imports: from __future__ import annotations
  from typing import Optional, Tuple, Union
  import numpy
  import tensorflow
  from ...activations_tf import get_tf_activation
  from ...modeling_tf_outputs import TFBaseModelOutputWithPast, TFCausalLMOutputWithPast
  from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFModelInputType, TFPreTrainedModel, TFSharedEmbeddings, 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, replace_return_docstrings
  from .configuration_opt import OPTConfig
label_desc: Use image node_id 4 for calling the TFOPTMainLayer obj's underlying member method code with example usage: obj.call(input_ids, attention_mask, head_mask, past_key_values, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict, training) and returns: TFBaseModelOutputWithPast, outputs