parameters: (graph, is_training, freeze_batch_norm_delay=None)
docstring: Finds batch norm layers and folds them into preceding layers.
  
    Folding only affects the following layers: Conv2D, fully connected, depthwise
    convolution.
  
    Args:
      graph: Graph to walk and modify.
      is_training: Bool, true if training.
      freeze_batch_norm_delay: How many steps to wait before freezing moving mean
        and variance and using them for batch normalization. This value is used
        only when is_training is True.
    Raises:
      ValueError: When batch norm folding fails.
docstring_summary: Finds batch norm layers and folds them into preceding layers.
docstring_tokens: Finds batch norm layers and folds them into preceding layers .
function: def FoldBatchNorms(graph, is_training, freeze_batch_norm_delay=None):
    """Finds batch norm layers and folds them into preceding layers.
  
    Folding only affects the following layers: Conv2D, fully connected, depthwise
    convolution.
  
    Args:
      graph: Graph to walk and modify.
      is_training: Bool, true if training.
      freeze_batch_norm_delay: How many steps to wait before freezing moving mean
        and variance and using them for batch normalization. This value is used
        only when is_training is True.
    Raises:
      ValueError: When batch norm folding fails.
    """
    _FoldFusedBatchNorms(
        graph, is_training, freeze_batch_norm_delay=freeze_batch_norm_delay)
    _FoldUnfusedBatchNorms(
        graph,
        is_training=is_training,
        freeze_batch_norm_delay=freeze_batch_norm_delay)
function_tokens: def FoldBatchNorms ( graph , is_training , freeze_batch_norm_delay = None ) : _FoldFusedBatchNorms ( graph , is_training , freeze_batch_norm_delay = freeze_batch_norm_delay ) _FoldUnfusedBatchNorms ( graph , is_training = is_training , freeze_batch_norm_delay = freeze_batch_norm_delay )