class_docstr: A list module to encapulate multiple RecMetric instances and provide the
  same interfaces as RecMetric.
  
  Args:
      rec_metrics (List[RecMetric]: the list of the input RecMetrics.
  
  Call Args:
      Not supported.
  
  Returns:
      Not supported.
  
  Example::
  
      ne = NEMetric(
               world_size=4,
               my_rank=0,
               batch_size=128,
               tasks=DefaultTaskInfo
           )
      metrics = RecMetricList([ne])
class_docstr_tok: ["A","list","module","to","encapulate","multiple","RecMetric","instances","and","provide","the","same","interfaces","as","RecMetric",".","Args",":","rec_metrics","(","List","[","RecMetric","]",":","the","list","of","the","input","RecMetrics",".","Call","Args",":","Not","supported",".","Returns",":","Not","supported",".","Example",":",":","ne","=","NEMetric","(","world_size=4",",","my_rank=0",",","batch_size=128",",","tasks=DefaultTaskInfo",")","metrics","=","RecMetricList","(","[","ne","]",")"]
code: def reset(self) -> None:
      for metric in self.rec_metrics:
          metric.reset()
  
imports: import abc
  import itertools
  import math
  from collections import defaultdict, deque
  from dataclasses import dataclass
  from enum import Enum
  from typing import Any, Callable, cast, Deque, Dict, Iterator, List, Mapping, Optional, Sequence, Set, Tuple, Type, TypeVar, Union
  import torch
  import torch.distributed
  import torch.nn
  from torch.profiler import record_function
  from torchmetrics import Metric
  from torchrec.distributed.types import get_tensor_size_bytes
  from torchrec.metrics.metrics_config import RecComputeMode, RecTaskInfo
  from torchrec.metrics.metrics_namespace import compose_metric_key, MetricNameBase, MetricNamespaceBase, MetricPrefix
label_desc: Use image node_id 10 for calling the RecMetricList obj's underlying member method code with example usage: obj.reset() without return types