code: def _get_conformer_negativer_sampler(
      input_dim: int,
      output_dim: int,
      num_negatives: int,
      cross_sample_negatives: int,
  ) -> NegativeSampler:
      """Build custom NegativeSampler module, including linear layer and negative sampling.
  
      Args:
          input_dim (int): Dimension of input after feature extraction.
          output_dim (int): Dimension of embedding for use in negative sampling. Same as the
              embedding in the feature projection.
          num_negatives (int): Number of negatives to sample.
          cross_sample_negatives (int): Number of cross sampled negatives.
  
      Returns:
          NegativeSampler:
              The resulting negative sampler module.
      """
      preprocessor = nn.Linear(input_dim, output_dim)
      return NegativeSampler(
          preprocessor, num_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 5 for calling a global function with example usage: _get_conformer_negativer_sampler(input_dim, output_dim, num_negatives, cross_sample_negatives) and returns: NegativeSampler