code: def call(
      self, hidden_states, output_hidden_states=False, return_dict=True
  ):
      all_hidden_states = () if output_hidden_states else None
  
      for i, layer_module in enumerate(self.stages):
          if output_hidden_states:
              all_hidden_states = all_hidden_states + (hidden_states,)
  
          hidden_states = layer_module(hidden_states)
  
      if output_hidden_states:
          all_hidden_states = all_hidden_states + (hidden_states,)
  
      if not return_dict:
          return tuple(
              v
              for v in [hidden_states, all_hidden_states]
              if v is not None
          )
  
      return TFBaseModelOutput(
          last_hidden_state=hidden_states,
          hidden_states=all_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 2 for calling the TFConvNextEncoder obj's underlying member method code with example usage: obj.call(hidden_states, output_hidden_states, return_dict) and returns: TFBaseModelOutput, tuple