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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def inception_v3_arg_scope(weight_decay=0.00004, stddev=0.1, batch_norm_var_collection='moving_vars'):
 """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. """

  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