class_docstr: Interface for a ranking model.
class_docstr_tok: ["Interface","for","a","ranking","model","."]
docstr: Calls transform_fn and returns dense Tensors.
docstr_tok: ["Calls","transform_fn","and","returns","dense","Tensors","."]
code: def _call_transform_fn(self, features, mode, params):
      """Calls transform_fn and returns dense Tensors."""
      transform_fn_args = function_utils.fn_args(self._transform_fn)
      if "mode" in transform_fn_args and "params" in transform_fn_args:
          return self._transform_fn(features, mode=mode, params=params)
      elif "mode" in transform_fn_args:
          return self._transform_fn(features, mode=mode)
      elif "params" in transform_fn_args:
          return self._transform_fn(features, params=params)
      else:
          return self._transform_fn(features)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  import six
  import tensorflow
  from tensorflow import estimator
  from tensorflow.python.util import function_utils
  from tensorflow_ranking.python import feature
  from tensorflow_ranking.python import utils
label_desc: Use image node_id 2 for calling the _RankingModel obj's underlying member method code with example usage: obj._call_transform_fn(features, mode, params) and returns: self, self, self, self