class_docstr: Holds the common metrics of a sequence-to-sequence model.
class_docstr_tok: ["Holds","the","common","metrics","of","a","sequence-to-sequence","model","."]
docstr: Reset the performance related metrics to their initial state.
docstr_tok: ["Reset","the","performance","related","metrics","to","their","initial","state","."]
code: def reset_performance_metrics(self) -> None:
      """Reset the performance related metrics to their initial state."""
      self.loss.reset()
      self.entropy_loss.reset()
      self.batch_size.reset()
      self.elements_per_batch.reset()
      self.elements_per_second.reset()
  
imports: from __future__ import annotations
  import math
  from abc import ABC, abstractmethod
  from dataclasses import dataclass
  from typing import Any, Optional, Sequence, Tuple, final
  import torch
  from torch import Tensor
  from torch.nn import Module
  from torcheval.metrics import Mean, Sum, Throughput
  from fairseq2.data import VocabularyInfo
  from fairseq2.gang import Gang
  from fairseq2.metrics import MetricBag
  from fairseq2.models.sequence import SequenceModelOutput
  from fairseq2.nn.padding import PaddingMask
  from fairseq2.typing import finaloverride
label_desc: Use image node_id 3 for calling the Seq2SeqModelMetricBag obj's underlying member method code with example usage: obj.reset_performance_metrics() without return types