code: def call(
      self,
      input_ids: TFModelInputType | None = 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,
      output_attentions: Optional[bool] = None,
      output_hidden_states: Optional[bool] = None,
      return_dict: Optional[bool] = None,
      training: Optional[bool] = False,
  ) -> Union[Tuple, TFBaseModelOutput]:
      if input_ids is not None and inputs_embeds is not None:
          raise ValueError(
              "You cannot specify both input_ids and inputs_embeds at the same time"
          )
      elif input_ids is not None:
          input_shape = shape_list(input_ids)
          input_ids = tf.reshape(input_ids, [-1, input_shape[-1]])
      elif inputs_embeds is not None:
          input_shape = shape_list(inputs_embeds)[:-1]
      else:
          raise ValueError(
              "You have to specify either input_ids or inputs_embeds"
          )
  
      if position_ids is None:
          position_ids = tf.expand_dims(
              tf.range(input_shape[-1]), axis=0
          )
  
      if attention_mask is not None:
          # We create a 3D attention mask from a 2D tensor mask.
          # Sizes are [batch_size, 1, 1, to_seq_length]
          # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
          # this attention mask is more simple than the triangular masking of causal attention
          # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
          attention_mask = tf.reshape(
              attention_mask, (input_shape[0], 1, 1, input_shape[1])
          )
  
          # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
          # masked positions, this operation will create a tensor which is 0.0 for
          # positions we want to attend and -10000.0 for masked positions.
          # Since we are adding it to the raw scores before the softmax, this is
          # effectively the same as removing these entirely.
  
          one_cst = tf.constant(1.0)
          attention_mask = tf.cast(attention_mask, dtype=one_cst.dtype)
          attention_mask = tf.multiply(
              tf.subtract(one_cst, attention_mask),
              tf.constant(-10000.0),
          )
      else:
          attention_mask = None
  
      # Prepare head mask if needed
      # 1.0 in head_mask indicate we keep the head
      # attention_probs has shape bsz x n_heads x N x N
      # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
      # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
      if head_mask is not None:
          raise NotImplementedError
      else:
          head_mask = [None] * self.num_hidden_layers
          # head_mask = tf.constant([0] * self.num_hidden_layers)
  
      position_ids = tf.reshape(
          position_ids, [-1, shape_list(position_ids)[-1]]
      )
  
      if inputs_embeds is None:
          check_embeddings_within_bounds(
              input_ids, self.config.vocab_size
          )
          inputs_embeds = self.tokens_embed(input_ids, mode="embedding")
      position_embeds = tf.gather(self.positions_embed, position_ids)
      if token_type_ids is not None:
          token_type_ids = tf.reshape(
              token_type_ids, [-1, shape_list(token_type_ids)[-1]]
          )
          check_embeddings_within_bounds(
              token_type_ids, self.config.vocab_size, "token_type_ids"
          )
          token_type_embeds = self.tokens_embed(
              token_type_ids, mode="embedding"
          )
      else:
          token_type_embeds = 0
      hidden_states = (
          inputs_embeds + position_embeds + token_type_embeds
      )
      hidden_states = self.drop(hidden_states, training=training)
  
      output_shape = input_shape + [shape_list(hidden_states)[-1]]
  
      all_attentions = () if output_attentions else None
      all_hidden_states = () if output_hidden_states else None
      for i, block in enumerate(self.h):
          if output_hidden_states:
              all_hidden_states = all_hidden_states + (
                  tf.reshape(hidden_states, output_shape),
              )
  
          outputs = block(
              hidden_states,
              attention_mask,
              head_mask[i],
              output_attentions,
              training=training,
          )
          hidden_states = outputs[0]
          if output_attentions:
              all_attentions = all_attentions + (outputs[1],)
  
      hidden_states = tf.reshape(hidden_states, output_shape)
      # Add last hidden state
      if output_hidden_states:
          all_hidden_states = all_hidden_states + (hidden_states,)
  
      if output_attentions:
          # let the number of heads free (-1) so we can extract attention even after head pruning
          attention_output_shape = (
              input_shape[:-1]
              + [-1]
              + shape_list(all_attentions[0])[-2:]
          )
          all_attentions = tuple(
              tf.reshape(t, attention_output_shape)
              for t in all_attentions
          )
  
      if not return_dict:
          return tuple(
              v
              for v in [
                  hidden_states,
                  all_hidden_states,
                  all_attentions,
              ]
              if v is not None
          )
  
      return TFBaseModelOutput(
          last_hidden_state=hidden_states,
          hidden_states=all_hidden_states,
          attentions=all_attentions,
      )
  
imports: from __future__ import annotations
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  import numpy
  import tensorflow
  from ...activations_tf import get_tf_activation
  from ...modeling_tf_outputs import TFBaseModelOutput, TFCausalLMOutput, TFSequenceClassifierOutput
  from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFConv1D, TFModelInputType, TFPreTrainedModel, TFSequenceClassificationLoss, TFSequenceSummary, TFSharedEmbeddings, get_initializer, keras_serializable, unpack_inputs
  from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
  from ...utils import ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_openai import OpenAIGPTConfig
label_desc: Use image node_id 6 for calling the TFOpenAIGPTMainLayer obj's underlying member method code with example usage: obj.call(input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, output_attentions, output_hidden_states, return_dict, training) and returns: TFBaseModelOutput, tuple