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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def get_metric_fns(metric_names, labels, outputs):
 """Generate a dictionary of metric name to metric function. Args: metric_names: list of strings in the format "prefix/metric_function_name". metric_function_name should refer to a function name in metrics.py. The prefix will be included in the key in the returned dict. labels: a tensor where batch is the first dimension. outputs: a tensor of model predictions, same dimensionality as labels. Returns: metric_fns: dict of metric functions keyed by their name. """

  metric_fns = {}
  for metric_name in metric_names:
    metric_fn_name = metric_name.split("/")[-1]
    if hasattr(metrics, metric_fn_name):
      metric_fn = getattr(metrics, metric_fn_name)
      metric_fns[metric_name] = metric_fn(labels, outputs)
    else:
      raise ValueError("Metric {} is not implemented".format(metric_fn_name))

  return metric_fns