code: def __init__(
      self,
      kernel_size: int = 3,
      strides: int = 1,
      padding: str = "same",
      **kwargs,
  ):
      super(DWConvBN, self).__init__(**kwargs)
      self.dw_conv = layers.DepthwiseConv2D(
          kernel_size=kernel_size,
          strides=strides,
          padding=padding,
          use_bias=False,
          kernel_regularizer=tf.keras.regularizers.l2(4e-5),
          name="dw1",
      )
      self.bn = layers.BatchNormalization(momentum=0.9, name="bn")
  
imports: import tensorflow
  from tensorflow.keras import layers, Model
label_desc: Use image node_id 1 to create a new DWConvBN object from inherited base classes: layers with example: obj = DWConvBN(kernel_size, strides, padding)