code: def create_alexnet():
      # Input variables denoting the features and label data
      feature_var = C.input_variable(
          (num_channels, image_height, image_width)
      )
      label_var = C.input_variable((num_classes))
  
      # apply model to input
      # remove mean value
      mean_removed_features = minus(
          feature_var, constant(114), name="mean_removed_input"
      )
  
      with default_options(activation=None, pad=True, bias=True):
          z = Sequential(
              [
                  # we separate Convolution and ReLU to name the output for feature extraction (usually before ReLU)
                  Convolution2D(
                      (11, 11),
                      96,
                      init=normal(0.01),
                      pad=False,
                      strides=(4, 4),
                      name="conv1",
                  ),
                  Activation(activation=relu, name="relu1"),
                  LocalResponseNormalization(
                      1.0, 2, 0.0001, 0.75, name="norm1"
                  ),
                  MaxPooling((3, 3), (2, 2), name="pool1"),
                  Convolution2D(
                      (5, 5),
                      192,
                      init=normal(0.01),
                      init_bias=0.1,
                      name="conv2",
                  ),
                  Activation(activation=relu, name="relu2"),
                  LocalResponseNormalization(
                      1.0, 2, 0.0001, 0.75, name="norm2"
                  ),
                  MaxPooling((3, 3), (2, 2), name="pool2"),
                  Convolution2D(
                      (3, 3), 384, init=normal(0.01), name="conv3"
                  ),
                  Activation(activation=relu, name="relu3"),
                  Convolution2D(
                      (3, 3),
                      384,
                      init=normal(0.01),
                      init_bias=0.1,
                      name="conv4",
                  ),
                  Activation(activation=relu, name="relu4"),
                  Convolution2D(
                      (3, 3),
                      256,
                      init=normal(0.01),
                      init_bias=0.1,
                      name="conv5",
                  ),
                  Activation(activation=relu, name="relu5"),
                  MaxPooling((3, 3), (2, 2), name="pool5"),
                  Dense(
                      4096,
                      init=normal(0.005),
                      init_bias=0.1,
                      name="fc6",
                  ),
                  Activation(activation=relu, name="relu6"),
                  Dropout(0.5, name="drop6"),
                  Dense(
                      4096,
                      init=normal(0.005),
                      init_bias=0.1,
                      name="fc7",
                  ),
                  Activation(activation=relu, name="relu7"),
                  Dropout(0.5, name="drop7"),
                  Dense(num_classes, init=normal(0.01), name="fc8"),
              ]
          )(mean_removed_features)
  
      # loss and metric
      ce = cross_entropy_with_softmax(z, label_var)
      pe = classification_error(z, label_var)
      pe5 = classification_error(z, label_var, topN=5)
  
      log_number_of_parameters(z)
      print()
  
      return {
          "feature": feature_var,
          "label": label_var,
          "ce": ce,
          "pe": pe,
          "pe5": pe5,
          "output": z,
      }
  
imports: from __future__ import print_function
  import os
  import math
  import argparse
  import numpy
  import cntk
  import _cntk_py
  from cntk.logging import *
  from cntk.train.training_session import *
  from cntk import *
  from cntk.train.distributed import *
  from cntk.io import ImageDeserializer, MinibatchSource, StreamDef, StreamDefs, FULL_DATA_SWEEP
  import cntk.io.transforms
  from cntk.layers import Convolution2D, Activation, MaxPooling, Dense, Dropout, default_options, Sequential
  from cntk.initializer import normal
label_desc: Use image node_id 3 for calling a global function with example usage: create_alexnet() and returns: dict