class_docstr: An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
  models.
class_docstr_tok: ["An","abstract","class","to","handle","weights","initialization","and","a","simple","interface","for","downloading","and","loading","pretrained","models","."]
docstr: Method used for serving the model.
  
  Args:
      inputs (`Dict[str, tf.Tensor]`):
          The input of the saved model as a dictionary of tensors.
docstr_tok: ["Method","used","for","serving","the","model",".","Args",":","inputs","(","`","Dict","[","str",",","tf.Tensor","]","`",")",":","The","input","of","the","saved","model","as","a","dictionary","of","tensors","."]
code: def serving(self, inputs):
      """
      Method used for serving the model.
  
      Args:
          inputs (`Dict[str, tf.Tensor]`):
              The input of the saved model as a dictionary of tensors.
      """
      output = self.call(inputs)
      return self.serving_output(output)
  
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 TFConvNextPreTrainedModel obj's underlying member method code with example usage: obj.serving(inputs) and returns: self