code: def inception_v3_arg_scope(weight_decay=0.00004,
                             stddev=0.1,
                             batch_norm_var_collection='moving_vars'):
    
    batch_norm_params = {
        # Decay for the moving averages.
        'decay': 0.9997,
        # epsilon to prevent 0s in variance.
        'epsilon': 0.001,
        # collection containing update_ops.
        'updates_collections': tf.GraphKeys.UPDATE_OPS,
        # collection containing the moving mean and moving variance.
        'variables_collections': {
            'beta': None,
            'gamma': None,
            'moving_mean': [batch_norm_var_collection],
            'moving_variance': [batch_norm_var_collection],
        }
    }
  
    # Set weight_decay for weights in Conv and FC layers.
    with slim.arg_scope([slim.conv2d, slim.fully_connected],
                        weights_regularizer=slim.l2_regularizer(weight_decay)):
      with slim.arg_scope([slim.conv2d],
                          weights_initializer=tf.truncated_normal_initializer(stddev=stddev),
                          activation_fn=tf.nn.relu, normalizer_fn=slim.batch_norm,
                          normalizer_params=batch_norm_params) as sc:
        return sc
docs: Defines the default InceptionV3 arg scope.
  
    Args:
      weight_decay: The weight decay to use for regularizing the model.
      stddev: The standard deviation of the trunctated normal weight initializer.
      batch_norm_var_collection: The name of the collection for the batch norm
        variables.
  
    Returns:
      An `arg_scope` to use for the inception v3 model.