class_docstr: RegNet shortcut, used to project the residual features to the correct size. If needed, it is also used to
  downsample the input using `stride=2`.
class_docstr_tok: ["RegNet","shortcut",",","used","to","project","the","residual","features","to","the","correct","size",".","If","needed",",","it","is","also","used","to","downsample","the","input","using","`","stride=2","`","."]
code: def call(
      self, inputs: tf.Tensor, training: bool = False
  ) -> tf.Tensor:
      return self.normalization(
          self.convolution(inputs), training=training
      )
  
imports: from typing import 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 TFRegNetShortCut obj's underlying member method code with example usage: obj.call(inputs, training) and returns: self