def load_transform_graph(self):
    """Load the transform graph without replacing any placeholders.

    This is necessary to ensure that variables in the transform graph are
    included in the training checkpoint when using tf.Estimator.  This should
    be called in the training input_fn.
    """
    if self._exported_as_v1 is None:
      self._exported_as_v1 = saved_transform_io.exported_as_v1(
          self.transform_savedmodel_dir)

    if self._exported_as_v1:
      saved_transform_io.partially_apply_saved_transform_internal(
          self.transform_savedmodel_dir, {})
    else:
      # Note: This should use the same mechanism as `transform_raw_features` to
      # load the SavedModel into the current graph context.
      _ = self.transform_features_layer()({})
Load the transform graph without replacing any placeholders.

    This is necessary to ensure that variables in the transform graph are
    included in the training checkpoint when using tf.Estimator.  This should
    be called in the training input_fn.
    
tensorflow_transform/output_wrapper.py