code: def _create_convolution_model_with_skip_level_links():
      with C.layers.default_options(
          init=C.glorot_uniform(), activation=C.relu
      ):
          h = feature_var
          # The first two layers has bias=False to test, the conversion
          # work with and without bias in the Convolution.
          a = C.layers.Convolution2D(
              filter_shape=(5, 5),
              num_filters=64,
              strides=(2, 2),
              pad=True,
              bias=False,
              name="first_convo",
          )(h)
  
          a = BatchNormalization(
              map_rank=1,
              normalization_time_constant=4096,
              use_cntk_engine=True,
              init_scale=1,
              disable_regularization=True,
          )(a)
  
          b = C.layers.Convolution2D(
              filter_shape=(5, 5),
              num_filters=64,
              strides=(2, 2),
              pad=True,
              bias=False,
              name="second_convo",
          )(h)
  
          b = BatchNormalization(
              map_rank=1,
              normalization_time_constant=4096,
              use_cntk_engine=True,
              init_scale=1,
              disable_regularization=True,
          )(b)
  
          h = a + b
  
          h = C.layers.Convolution2D(
              filter_shape=(5, 5),
              num_filters=64,
              strides=(1, 1),
              pad=True,
              name="thrid_convo",
          )(h)
  
          h = BatchNormalization(
              map_rank=1,
              normalization_time_constant=4096,
              use_cntk_engine=True,
              init_scale=1,
              disable_regularization=True,
          )(h)
  
          h = C.layers.Convolution2D(
              filter_shape=(5, 5),
              num_filters=64,
              strides=(1, 1),
              pad=True,
              name="fourth_convo",
          )(h)
  
          h = BatchNormalization(
              map_rank=1,
              normalization_time_constant=4096,
              use_cntk_engine=True,
              init_scale=1,
              disable_regularization=True,
          )(h)
  
          r = C.layers.Dense(
              num_classes, activation=None, name="classify"
          )(h)
      return r
  
file_path: CNTK/bindings/python/cntk/contrib/netopt/test/quantization_test.py
imports: import numpy
  import pytest
  import cntk
  import cntk.contrib.netopt.quantization
  from cntk.layers import BatchNormalization
label_desc: Use image node_id 2 for calling a global function with example usage: _create_convolution_model_with_skip_level_links() and returns: r