code: def conv3_block(input, k=1, dropout=0.0):
      init = input
  
      # channel_axis = 1 if K.image_dim_ordering() == "th" else -1
      channel_axis = -1
  
      x = keras.layers.BatchNormalization(
          axis=channel_axis,
          momentum=0.1,
          epsilon=1e-5,
          gamma_initializer="uniform",
      )(input)
      x = keras.layers.Activation("relu")(x)
      x = keras.layers.Convolution2D(
          64 * k,
          (3, 3),
          padding="same",
          kernel_initializer="he_normal",
          kernel_regularizer=keras.regularizers.l2(weight_decay),
          use_bias=False,
      )(x)
  
      if dropout > 0.0:
          x = keras.layers.Dropout(dropout)(x)
  
      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(
          64 * k,
          (3, 3),
          padding="same",
          kernel_initializer="he_normal",
          kernel_regularizer=keras.regularizers.l2(weight_decay),
          use_bias=False,
      )(x)
  
      m = keras.layers.Add()([init, x])
      return m
  
imports: from ..imports import *
label_desc: Use image node_id 5 for calling a global function with example usage: conv3_block(input, k, dropout) and returns: m