class_docstr: RegNet's Y layer: an X layer with Squeeze and Excitation.
class_docstr_tok: ["RegNet","'s","Y","layer",":","an","X","layer","with","Squeeze","and","Excitation","."]
code: def build(self, input_shape=None):
      if self.built:
          return
      self.built = True
      if getattr(self, "shortcut", None) is not None:
          with tf.name_scope(self.shortcut.name):
              self.shortcut.build(None)
      if getattr(self, "layers", None) is not None:
          for layer in self.layers:
              with tf.name_scope(layer.name):
                  layer.build(None)
  
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 3 for calling the TFRegNetYLayer obj's underlying member method code with example usage: obj.build(input_shape) without return types