code: def serving_output(
      self, output: TFSequenceClassifierOutput
  ) -> TFSequenceClassifierOutput:
      # hidden_states not converted to Tensor with tf.convert_to_tensor as they are all of different dimensions
      return TFSequenceClassifierOutput(
          logits=output.logits, hidden_states=output.hidden_states
      )
  
imports: from typing import Dict, Optional, Tuple, Union
  import tensorflow
  from ...activations_tf import ACT2FN
  from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
  from ...modeling_tf_outputs import TFBaseModelOutputWithNoAttention, TFBaseModelOutputWithPoolingAndNoAttention, TFSequenceClassifierOutput
  from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
  from ...tf_utils import shape_list
  from ...utils import logging
  from .configuration_regnet import RegNetConfig
label_desc: Use image node_id 3 for calling the TFRegNetForImageClassification obj's underlying member method code with example usage: obj.serving_output(output) and returns: TFSequenceClassifierOutput