code: def _call_eval_metrics(eval_metrics):
      if not eval_metrics:
          return {}
      fn, args = eval_metrics
      if isinstance(args, dict):
          return fn(**args)
      else:
          return fn(*args)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import inspect
  from absl import logging
  from adanet import tf_compat
  import six
  import tensorflow.compat.v2
label_desc: Use image node_id 1 for calling a global function with example usage: _call_eval_metrics(eval_metrics) and returns: dict, fn, fn