class_docstr: Holds the loss of a wav2vec 2.0 model.
class_docstr_tok: ["Holds","the","loss","of","a","wav2vec","2.0","model","."]
docstr: Compute the gradient of the loss.
docstr_tok: ["Compute","the","gradient","of","the","loss","."]
code: def backward(self) -> None:
      """Compute the gradient of the loss."""
      self.total.backward()
  
imports: from __future__ import annotations
  from dataclasses import dataclass
  from typing import Optional, Tuple
  import torch
  from torch import Tensor
  from torch.nn import Module
  from torch.nn.functional import cross_entropy
  from fairseq2.models.sequence import SequenceBatch
  from fairseq2.models.wav2vec2.frontend import Wav2Vec2Frontend
  from fairseq2.models.wav2vec2.masker import Wav2Vec2Masker, extract_masked_elements
  from fairseq2.models.wav2vec2.vector_quantizer import VectorQuantizer, VectorQuantizerOutput
  from fairseq2.nn.ops import repeat_interleave
  from fairseq2.nn.padding import PaddingMask
  from fairseq2.nn.projection import Linear
  from fairseq2.nn.transformer import TransformerEncoder
  from fairseq2.typing import DataType, Device
label_desc: Use image node_id 1 for calling the Wav2Vec2Loss obj's underlying member method code with example usage: obj.backward() without return types