class_docstr: Applies preprocessing to input and then computes negative sampling.
  
  Args:
      preprocessor (nn.Module): Transforms input tensor prior to negative sampling.
      num_negatives (int): Number of negative examples to sample.
      cross_sample_negatives (int): Number of negative examples to cross sample.
code: def __init__(
      self,
      preprocessor: Module,
      num_negatives: int,
      cross_sample_negatives: int,
  ):
      super().__init__()
      self.preprocessor = preprocessor
      self.num_negatives = num_negatives
      self.cross_sample_negatives = cross_sample_negatives
  
imports: from typing import List, Optional, Tuple, Union
  import torch
  from torch import nn, Tensor
  from torch.nn import Module, ModuleList
  from torchaudio.models import Wav2Vec2Model
  from torchaudio.models.conformer import ConformerLayer
  from torchaudio.models.rnnt import _TimeReduction
  from torchaudio.models.wav2vec2 import components
label_desc: Use image node_id 1 to create a new NegativeSampler object from inherited base classes: Module with example: obj = NegativeSampler(preprocessor, num_negatives, cross_sample_negatives)