class_docstr: Wrapper class to compose class: `ConcatDataset` and multiple
  :class:`BaseMetric` instances.
  The metrics will be evaluated on each dataset slice separately. The name of
  the each metric is the concatenation of the dataset prefix, the metric
  prefix and the key of metric - e.g.
  `dataset_prefix/metric_prefix/accuracy`.
  
  Args:
      metrics (dict or BaseMetric or Sequence): The config of metrics.
      dataset_prefixes (Sequence[str]): The prefix of each dataset. The
          length of this sequence should be the same as the length of the
          datasets.
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code: def __init__(
      self,
      metrics: Union[ConfigType, BaseMetric, Sequence],
      dataset_prefixes: Sequence[str],
  ) -> None:
      super().__init__(metrics)
      self.dataset_prefixes = dataset_prefixes
  
file_path: mmocr/mmocr/evaluation/evaluator/multi_datasets_evaluator.py
imports: import warnings
  from collections import OrderedDict
  from typing import Sequence, Union
  from mmengine.dist import broadcast_object_list, collect_results, is_main_process
  from mmengine.evaluator import BaseMetric, Evaluator
  from mmengine.evaluator.metric import _to_cpu
  from mmocr.registry import EVALUATOR
  from mmocr.utils.typing_utils import ConfigType
label_desc: Use image node_id 1 to create a new MultiDatasetsEvaluator object from inherited base classes: Evaluator with example: obj = MultiDatasetsEvaluator(metrics, dataset_prefixes)