class_docstr: Fetch extra tensors **along with** each training step,
  and call some function over the values.
  It uses ``_{before,after}_run`` method to inject ``tf.train.SessionRunHooks``
  to the session.
  You can use it to print tensors, save tensors to file, etc.
  
  Example:
  
  .. code-block:: python
  
      ProcessTensors(['mycost1', 'mycost2'], lambda c1, c2: print(c1, c2, c1 + c2))
class_docstr_tok: ["Fetch","extra","tensors","*","*","along","with","*","*","each","training","step",",","and","call","some","function","over","the","values",".","It","uses","``","_","{","before",",","after","}","_run","``","method","to","inject","``","tf.train.SessionRunHooks","``","to","the","session",".","You","can","use","it","to","print","tensors",",","save","tensors","to","file",",","etc",".","Example",":","..","code-block",":",":","python","ProcessTensors","(","[","'mycost1","'",",","'mycost2","'","]",",","lambda","c1",",","c2",":","print","(","c1",",","c2",",","c1","+","c2",")",")"]
code: def _after_run(self, _, rv):
      results = rv.results
      self._fn(*results)
  
imports: import numpy
  import os
  from ..compat import tfv1
  from ..tfutils.common import get_op_tensor_name
  from ..utils import logger
  from .base import Callback
label_desc: Use image node_id 4 for calling the ProcessTensors obj's underlying member method code with example usage: obj._after_run(_, rv) without return types