code: def call(
      self,
      x,
      attention_mask,
      head_mask,
      output_attentions,
      training=False,
  ):
      output_attn = self.attn(
          x,
          attention_mask,
          head_mask,
          output_attentions,
          training=training,
      )
      a = output_attn[0]  # output_attn: a, (attentions)
  
      n = self.ln_1(x + a)
      m = self.mlp(n, training=training)
      h = self.ln_2(n + m)
  
      outputs = [h] + output_attn[1:]
      return outputs
  
imports: 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 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 2 for calling the TFBlock obj's underlying member method code with example usage: obj.call(x, attention_mask, head_mask, output_attentions, training) and returns: outputs