class_docstr: Logger for model metrics
class_docstr_tok: ["Logger","for","model","metrics"]
code: def __init__(self, group, print_freq=1):
      self.print_freq = print_freq
      self._iter = 0
      self.data = defaultdict(lambda: deque(maxlen=self.print_freq))
      self.data["group"].append(group)
  
file_path: audio/examples/pipeline_wavernn/utils.py
imports: import logging
  import os
  import shutil
  from collections import defaultdict, deque
  import torch
label_desc: Use image node_id 1 to create a new MetricLogger object  with example: obj = MetricLogger(group, print_freq)