class_docstr: Base class for all predictors.
  
  Attributes:
      return_input (bool): whether the call will also return (inputs, outputs)
          or just outputs
docstr: Call the predictor on some inputs.
  
  Example:
      When you have a predictor defined with two inputs, call it with:
  
      .. code-block:: python
  
          predictor(e1, e2)
  
  Returns:
      list[array]: list of outputs
docstr_tok: ["Call","the","predictor","on","some","inputs",".","Example",":","When","you","have","a","predictor","defined","with","two","inputs",",","call","it","with",":","..","code-block",":",":","python","predictor","(","e1",",","e2",")","Returns",":","list","[","array","]",":","list","of","outputs"]
code: def __call__(self, *dp):
      """
      Call the predictor on some inputs.
  
      Example:
          When you have a predictor defined with two inputs, call it with:
  
          .. code-block:: python
  
              predictor(e1, e2)
  
      Returns:
          list[array]: list of outputs
      """
      output = self._do_call(dp)
      if self.return_input:
          return (dp, output)
      else:
          return output
  
imports: from abc import ABCMeta, abstractmethod
  import six
  import tensorflow
  from ..input_source import PlaceholderInput
  from ..tfutils.common import get_tensors_by_names, get_op_tensor_name
  from ..tfutils.tower import PredictTowerContext
label_desc: Use image node_id 1 for calling the PredictorBase obj's underlying member method code with example usage: obj.__call__() and returns: dp,  output, output