tensorflow_examples/profiling/resnet_model.py [118:144]:
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      padding='same',
      use_bias=False,
      kernel_initializer='he_normal',
      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
      name=conv_name_base + '2b')(
          x)
  x = layers.BatchNormalization(
      axis=bn_axis,
      momentum=BATCH_NORM_DECAY,
      epsilon=BATCH_NORM_EPSILON,
      name=bn_name_base + '2b')(
          x)
  x = layers.Activation('relu')(x)

  x = layers.Conv2D(
      filters3, (1, 1),
      use_bias=False,
      kernel_initializer='he_normal',
      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
      name=conv_name_base + '2c')(
          x)
  x = layers.BatchNormalization(
      axis=bn_axis,
      momentum=BATCH_NORM_DECAY,
      epsilon=BATCH_NORM_EPSILON,
      name=bn_name_base + '2c')(
          x)
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tensorflow_examples/profiling/resnet_model.py [203:229]:
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      padding='same',
      use_bias=False,
      kernel_initializer='he_normal',
      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
      name=conv_name_base + '2b')(
          x)
  x = layers.BatchNormalization(
      axis=bn_axis,
      momentum=BATCH_NORM_DECAY,
      epsilon=BATCH_NORM_EPSILON,
      name=bn_name_base + '2b')(
          x)
  x = layers.Activation('relu')(x)

  x = layers.Conv2D(
      filters3, (1, 1),
      use_bias=False,
      kernel_initializer='he_normal',
      kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
      name=conv_name_base + '2c')(
          x)
  x = layers.BatchNormalization(
      axis=bn_axis,
      momentum=BATCH_NORM_DECAY,
      epsilon=BATCH_NORM_EPSILON,
      name=bn_name_base + '2c')(
          x)
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