code: def expand_conv(init, base, k, strides=(1, 1)):
      x = keras.layers.Convolution2D(
          base * k,
          (3, 3),
          padding="same",
          strides=strides,
          kernel_initializer="he_normal",
          kernel_regularizer=keras.regularizers.l2(weight_decay),
          use_bias=False,
      )(init)
  
      channel_axis = (
          1 if K.image_data_format() == "channels_first" else -1
      )
  
      x = keras.layers.BatchNormalization(
          axis=channel_axis,
          momentum=0.1,
          epsilon=1e-5,
          gamma_initializer="uniform",
      )(x)
      x = keras.layers.Activation("relu")(x)
  
      x = keras.layers.Convolution2D(
          base * k,
          (3, 3),
          padding="same",
          kernel_initializer="he_normal",
          kernel_regularizer=keras.regularizers.l2(weight_decay),
          use_bias=False,
      )(x)
  
      skip = keras.layers.Convolution2D(
          base * k,
          (1, 1),
          padding="same",
          strides=strides,
          kernel_initializer="he_normal",
          kernel_regularizer=keras.regularizers.l2(weight_decay),
          use_bias=False,
      )(init)
  
      m = keras.layers.Add()([x, skip])
  
      return m
  
imports: from ..imports import *
label_desc: Use image node_id 2 for calling a global function with example usage: expand_conv(init, base, k, strides) and returns: m