code: def _sample_negatives(
      input: Tensor, num_negatives: int, cross_sample_negatives: int
  ) -> Tuple[Tensor, Tensor]:
      """Sample negative examples from masked input.
  
      Args:
          input (Tensor): Tensor of dimension `(batch, frame, dim)`.
          num_negatives (int): Number of negative examples to sample.
          cross_sample_negatives (int): Number of negative examples to cross sample.
  
      Returns:
          (Tensor, Tensor):
          Tensor
              The negative samples.
          Tensor
              The indices of the negative samples.
      """
      if num_negatives == 0 and cross_sample_negatives == 0:
          return (
              torch.zeros(0).to(input.device, input.dtype),
              torch.zeros(0).to(input.device, input.dtype),
          )
  
      B, T, D = input.shape
      input = input.view(-1, D)
  
      cross_high = T * B
      high = T
  
      assert high > 1
  
      if num_negatives > 0:
          tszs = (
              _buffered_arange(T)
              .unsqueeze(-1)
              .expand(-1, num_negatives)
              .flatten()
          )
  
          neg_idxs = torch.randint(
              low=0, high=high - 1, size=(B, num_negatives * T)
          )
          neg_idxs[neg_idxs >= tszs] += 1
  
      if cross_sample_negatives > 0:
          tszs = (
              _buffered_arange(T)
              .unsqueeze(-1)
              .expand(-1, cross_sample_negatives)
              .flatten()
          )
  
          cross_neg_idxs = torch.randint(
              low=0,
              high=cross_high - 1,
              size=(B, cross_sample_negatives * T),
          )
          cross_neg_idxs[cross_neg_idxs >= tszs] += 1
  
      if num_negatives > 0:
          neg_idxs = neg_idxs + (torch.arange(B).unsqueeze(1) * high)
      else:
          neg_idxs = cross_neg_idxs
  
      if cross_sample_negatives > 0 and num_negatives > 0:
          neg_idxs = torch.cat([neg_idxs, cross_neg_idxs], dim=1)
  
      negs = input[neg_idxs.view(-1)]
      negs = negs.view(
          B, T, num_negatives + cross_sample_negatives, D
      ).permute(
          2, 0, 1, 3
      )  # NxBxCxT
  
      return negs, neg_idxs
  
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 2 for calling a global function with example usage: _sample_negatives(input, num_negatives, cross_sample_negatives) and returns: negs,  neg_idxs, torch,  torch