class_docstr: ConvNext stage, consisting of an optional downsampling layer + multiple residual blocks.
  
  Args:
      config ([`ConvNextConfig`]): Model configuration class.
      in_channels (`int`): Number of input channels.
      out_channels (`int`): Number of output channels.
      depth (`int`): Number of residual blocks.
      drop_path_rates(`List[float]`): Stochastic depth rates for each layer.
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code: def call(self, hidden_states):
      for layer in self.downsampling_layer:
          hidden_states = layer(hidden_states)
      for layer in self.layers:
          hidden_states = layer(hidden_states)
      return 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 TFConvNextStage obj's underlying member method code with example usage: obj.call(hidden_states) and returns: hidden_states