class_docstr: Defines the interface for TF metrics.
docstr: Accumulates statistics for the metric. Users should use __call__ instead.
  
  Note: This function is executed as a graph function in graph mode.
  This means:
  a) Operations on the same resource are executed in textual order.
     This should make it easier to do things like add the updated
     value of a variable to another, for example.
  b) You don't need to worry about collecting the update ops to execute.
     All update ops added to the graph by this function will be executed.
  As a result, code should generally work the same way with graph or
  eager execution.
  
  Args:
    *args:
    **kwargs: A mini-batch of inputs to the Metric, as passed to `__call__()`.
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code: def call(self, *args, **kwargs):
      """Accumulates statistics for the metric. Users should use __call__ instead.
  
      Note: This function is executed as a graph function in graph mode.
      This means:
      a) Operations on the same resource are executed in textual order.
         This should make it easier to do things like add the updated
         value of a variable to another, for example.
      b) You don't need to worry about collecting the update ops to execute.
         All update ops added to the graph by this function will be executed.
      As a result, code should generally work the same way with graph or
      eager execution.
  
      Args:
        *args:
        **kwargs: A mini-batch of inputs to the Metric, as passed to `__call__()`.
      """
      raise NotImplementedError(
          "Metrics must define a call() member function"
      )
  
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 2 for calling the TFStepMetric obj's underlying member method code with example usage: obj.call() without return types