code: def build(self, inputs_shape):
      if self.in_channels is None:
          self.in_channels = inputs_shape[1]
  
      self.weight = self._get_weights(
          self.w_name,
          self.kaiming,
          [self.in_channels, self.out_channels],
          regularization=self.regularization != 0,
      )
      self.bias = self._get_weights(
          self.b_name,
          self.kaiming,
          [self.out_channels],
          regularization=self.regularization != 0,
      )  # equal to b2leaky_relu
      self.weight = (
          tf.clip_by_norm(self.weight, 1)
          if self.max_norm
          else self.weight
      )
  
      self.weight = self.mask_weights(self.weight)
  
imports: import numpy
  import tensorflow
  from tensorlayer.layers import Layer, Dropout, Dense, Input, BatchNorm, Reshape, Elementwise
  from tensorlayer.models import Model
  from tensorlayer import logging
  from .common import mask_weight, neighbour_matrix
label_desc: Use image node_id 2 for calling the Mask_layer obj's underlying member method code with example usage: obj.build(inputs_shape) without return types