class_docstr: 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 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 2 for calling the TFRegNetPreTrainedModel obj's underlying member method code with example usage: obj.serving(inputs) and returns: self