code: def call(
      self,
      input_ids: TFModelInputType | None = None,
      attention_mask: np.ndarray | tf.Tensor | None = None,
      position_ids: np.ndarray | tf.Tensor | None = None,
      encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
      encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
      head_mask: np.ndarray | tf.Tensor | None = None,
      cross_attn_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: Any,
  ) -> Union[
      TFBaseModelOutputWithPastAndCrossAttentions, 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
      )
  
      # retrieve input_ids and inputs_embeds
      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 = tf.shape(input_ids)
          input_ids = tf.reshape(input_ids, (-1, input_shape[-1]))
      elif inputs_embeds is not None:
          input_shape = tf.shape(inputs_embeds)[:-1]
      else:
          raise ValueError(
              "You have to specify either input_ids or inputs_embeds"
          )
  
      past_key_values_length = (
          past_key_values[0][0].shape[2]
          if past_key_values is not None
          else 0
      )
  
      if position_ids is None:
          position_ids = tf.expand_dims(
              tf.range(
                  past_key_values_length,
                  input_shape[-1] + past_key_values_length,
              ),
              axis=0,
          )
      position_ids = tf.reshape(
          position_ids, [-1, shape_list(position_ids)[-1]]
      )
  
      if inputs_embeds is None:
          check_embeddings_within_bounds(
              input_ids, self.embed_tokens.vocab_size
          )
          inputs_embeds = (
              self.embed_tokens(input_ids) * self.embed_scale
          )
  
      attention_mask = self._prepare_decoder_attention_mask(
          attention_mask, input_shape, past_key_values_length
      )
  
      # expand encoder attention mask
      if (
          encoder_hidden_states is not None
          and encoder_attention_mask is not None
      ):
          # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
          encoder_attention_mask = _expand_mask(
              encoder_attention_mask, tgt_len=input_shape[-1]
          )
  
      # embed positions
      positions = self.embed_positions(position_ids)
  
      hidden_states = (
          tf.cast(inputs_embeds, dtype=tf.float32) + positions
      )
  
      hidden_states = self.dropout(hidden_states, training=training)
  
      # decoder layers
      all_hidden_states = () if output_hidden_states else None
      all_self_attns = () if output_attentions else None
      all_cross_attentions = (
          ()
          if (output_attentions and encoder_hidden_states is not None)
          else None
      )
      next_decoder_cache = () if use_cache else None
  
      # check if head_mask and cross_attn_head_mask have a correct number of layers specified if desired
      for attn_mask_name, attn_mask in [
          ("head_mask", head_mask),
          ("cross_attn_head_mask", cross_attn_head_mask),
      ]:
          if attn_mask is not None:
              tf.debugging.assert_equal(
                  shape_list(attn_mask)[0],
                  len(self.layers),
                  message=(
                      f"The {attn_mask_name} should be specified for {len(self.layers)} layers, but it is for"
                      f" {shape_list(attn_mask)[0]}."
                  ),
              )
  
      for idx, decoder_layer in enumerate(self.layers):
          # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
          if output_hidden_states:
              all_hidden_states += (hidden_states,)
  
          dropout_probability = random.uniform(0, 1)
          if training and (dropout_probability < self.layerdrop):
              continue
  
          past_key_value = (
              past_key_values[idx]
              if past_key_values is not None
              else None
          )
  
          (
              hidden_states,
              layer_self_attn,
              layer_cross_attn,
              present_key_value,
          ) = decoder_layer(
              hidden_states,
              attention_mask=attention_mask,
              encoder_hidden_states=encoder_hidden_states,
              encoder_attention_mask=encoder_attention_mask,
              layer_head_mask=(
                  head_mask[idx] if head_mask is not None else None
              ),
              cross_attn_layer_head_mask=(
                  cross_attn_head_mask[idx]
                  if cross_attn_head_mask is not None
                  else None
              ),
              past_key_value=past_key_value,
          )
  
          if use_cache:
              next_decoder_cache += (present_key_value,)
  
          if output_attentions:
              all_self_attns += (layer_self_attn,)
  
              if encoder_hidden_states is not None:
                  all_cross_attentions += (layer_cross_attn,)
  
      hidden_states = self.layer_norm(hidden_states)
  
      # add hidden states from the last decoder layer
      if output_hidden_states:
          all_hidden_states += (hidden_states,)
  
      next_cache = next_decoder_cache if use_cache else None
      if not return_dict:
          return tuple(
              v
              for v in [
                  hidden_states,
                  next_cache,
                  all_hidden_states,
                  all_self_attns,
                  all_cross_attentions,
              ]
              if v is not None
          )
      return TFBaseModelOutputWithPastAndCrossAttentions(
          last_hidden_state=hidden_states,
          past_key_values=next_cache,
          hidden_states=all_hidden_states,
          attentions=all_self_attns,
          cross_attentions=all_cross_attentions,
      )
  
imports: from __future__ import annotations
  import math
  import random
  from typing import Any, Optional, Tuple, Union
  import numpy
  import tensorflow
  from ...activations_tf import get_tf_activation
  from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from ...modeling_tf_outputs import TFBaseModelOutputWithPastAndCrossAttentions, TFCausalLMOutputWithCrossAttentions
  from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFModelInputType, TFPreTrainedModel, TFSharedEmbeddings, get_initializer, keras_serializable, unpack_inputs
  from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
  from ...utils import logging
  from .configuration_xglm import XGLMConfig
label_desc: Use image node_id 6 for calling the TFXGLMMainLayer obj's underlying member method code with example usage: obj.call(input_ids, attention_mask, position_ids, encoder_hidden_states, encoder_attention_mask, head_mask, cross_attn_head_mask, past_key_values, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict, training) and returns: TFBaseModelOutputWithPastAndCrossAttentions, tuple