code: def serving_output(
      self, output: TFSequenceClassifierOutput
  ) -> TFSequenceClassifierOutput:
      hs = (
          tf.convert_to_tensor(output.hidden_states)
          if self.config.output_hidden_states
          else None
      )
      attns = (
          tf.convert_to_tensor(output.attentions)
          if self.config.output_attentions
          else None
      )
  
      return TFSequenceClassifierOutput(
          logits=output.logits, hidden_states=hs, attentions=attns
      )
  
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 3 for calling the TFOpenAIGPTForSequenceClassification obj's underlying member method code with example usage: obj.serving_output(output) and returns: TFSequenceClassifierOutput