code: def create_wide_residual_network(
      input_dim,
      nb_classes=100,
      N=2,
      k=1,
      activation="softmax",
      dropout=0.0,
      verbose=1,
  ):
      """
      Creates a Wide Residual Network with specified parameters
  
      :param input: Input Keras object
      :param nb_classes: Number of output classes
      :param N: Depth of the network. Compute N = (n - 4) / 6.
                Example : For a depth of 16, n = 16, N = (16 - 4) / 6 = 2
                Example2: For a depth of 28, n = 28, N = (28 - 4) / 6 = 4
                Example3: For a depth of 40, n = 40, N = (40 - 4) / 6 = 6
      :param k: Width of the network.
      :param dropout: Adds dropout if value is greater than 0.0
      :param verbose: Debug info to describe created WRN
      :return:
      """
      channel_axis = (
          1 if K.image_data_format() == "channels_first" else -1
      )
  
      ip = keras.layers.Input(shape=input_dim)
  
      x = initial_conv(ip)
      nb_conv = 4
  
      x = expand_conv(x, 16, k)
      nb_conv += 2
  
      for i in range(N - 1):
          x = conv1_block(x, k, dropout)
          nb_conv += 2
  
      x = keras.layers.BatchNormalization(
          axis=channel_axis,
          momentum=0.1,
          epsilon=1e-5,
          gamma_initializer="uniform",
      )(x)
      x = keras.layers.Activation("relu")(x)
  
      x = expand_conv(x, 32, k, strides=(2, 2))
      nb_conv += 2
  
      for i in range(N - 1):
          x = conv2_block(x, k, dropout)
          nb_conv += 2
  
      x = keras.layers.BatchNormalization(
          axis=channel_axis,
          momentum=0.1,
          epsilon=1e-5,
          gamma_initializer="uniform",
      )(x)
      x = keras.layers.Activation("relu")(x)
  
      x = expand_conv(x, 64, k, strides=(2, 2))
      nb_conv += 2
  
      for i in range(N - 1):
          x = conv3_block(x, k, dropout)
          nb_conv += 2
  
      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.AveragePooling2D((8, 8))(x)
      x = keras.layers.Flatten()(x)
  
      x = keras.layers.Dense(
          nb_classes,
          kernel_regularizer=keras.regularizers.l2(weight_decay),
          activation=activation,
      )(x)
  
      model = keras.Model(ip, x)
  
      if verbose:
          print("Wide Residual Network-%d-%d created." % (nb_conv, k))
      return model
  
imports: from ..imports import *
label_desc: Use image node_id 6 for calling a global function with example usage: create_wide_residual_network(input_dim, nb_classes, N, k, activation, dropout, verbose) and returns: model