code: def serving_output(
      self, output: TFBaseModelOutputWithPooling
  ) -> TFBaseModelOutputWithPooling:
      # hidden_states not converted to Tensor with tf.convert_to_tensor as they are all of different dimensions
      return TFBaseModelOutputWithPooling(
          last_hidden_state=output.last_hidden_state,
          pooler_output=output.pooler_output,
          hidden_states=output.hidden_states,
      )
  
imports: from typing import Dict, Optional, Tuple, Union
  import numpy
  import tensorflow
  from transformers import shape_list
  from ...activations_tf import get_tf_activation
  from ...modeling_tf_outputs import TFBaseModelOutput, TFBaseModelOutputWithPooling, TFSequenceClassifierOutput
  from ...modeling_tf_utils import TFModelInputType, TFPreTrainedModel, TFSequenceClassificationLoss, get_initializer, keras_serializable, unpack_inputs
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_convnext import ConvNextConfig
label_desc: Use image node_id 3 for calling the TFConvNextModel obj's underlying member method code with example usage: obj.serving_output(output) and returns: TFBaseModelOutputWithPooling