code: def inception_arg_scope(
      weight_decay=0.00004,
      use_batch_norm=True,
      batch_norm_decay=0.9997,
      batch_norm_epsilon=0.001,
      activation_fn=tf.nn.relu,
      batch_norm_updates_collections=tf.GraphKeys.UPDATE_OPS,
      batch_norm_scale=False,
  ):
      """Defines the default arg scope for inception models.
  
      Args:
        weight_decay: The weight decay to use for regularizing the model.
        use_batch_norm: "If `True`, batch_norm is applied after each convolution.
        batch_norm_decay: Decay for batch norm moving average.
        batch_norm_epsilon: Small float added to variance to avoid dividing by zero
          in batch norm.
        activation_fn: Activation function for conv2d.
        batch_norm_updates_collections: Collection for the update ops for
          batch norm.
        batch_norm_scale: If True, uses an explicit `gamma` multiplier to scale the
          activations in the batch normalization layer.
  
      Returns:
        An `arg_scope` to use for the inception models.
      """
      batch_norm_params = {
          # Decay for the moving averages.
          "decay": batch_norm_decay,
          # epsilon to prevent 0s in variance.
          "epsilon": batch_norm_epsilon,
          # collection containing update_ops.
          "updates_collections": batch_norm_updates_collections,
          # use fused batch norm if possible.
          "fused": None,
          "scale": batch_norm_scale,
      }
      if use_batch_norm:
          normalizer_fn = slim.batch_norm
          normalizer_params = batch_norm_params
      else:
          normalizer_fn = None
          normalizer_params = {}
      # 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=slim.variance_scaling_initializer(),
              activation_fn=activation_fn,
              normalizer_fn=normalizer_fn,
              normalizer_params=normalizer_params,
          ) as sc:
              return sc
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
label_desc: Use image node_id 1 for calling a global function with example usage: inception_arg_scope(weight_decay, use_batch_norm, batch_norm_decay, batch_norm_epsilon, activation_fn, batch_norm_updates_collections, batch_norm_scale) and returns: sc