class_docstr: Defines the interface for TF metrics.
docstr: Returns op to execute to update this metric for these inputs.
  
  Returns None if eager execution is enabled.
  Returns a graph-mode function if graph execution is enabled.
  
  Args:
    *args:
    **kwargs: A mini-batch of inputs to the Metric, passed on to `call()`.
docstr_tok: ["Returns","op","to","execute","to","update","this","metric","for","these","inputs",".","Returns","None","if","eager","execution","is","enabled",".","Returns","a","graph-mode","function","if","graph","execution","is","enabled",".","Args",":","*","args",":","*","*","kwargs",":","A","mini-batch","of","inputs","to","the","Metric",",","passed","on","to","`","call","(",")","`","."]
code: def __call__(self, *args, **kwargs):
      """Returns op to execute to update this metric for these inputs.
  
      Returns None if eager execution is enabled.
      Returns a graph-mode function if graph execution is enabled.
  
      Args:
        *args:
        **kwargs: A mini-batch of inputs to the Metric, passed on to `call()`.
      """
      return self._update_state(*args, **kwargs)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tf_agents.utils import common
label_desc: Use image node_id 7 for calling the TFStepMetric obj's underlying member method code with example usage: obj.__call__() and returns: self