code: def sample_timesteps(x, num_timesteps):
      """Returns a random sample of timesteps as a 1-D tensor
      (one dimension only)
  
      Arguments
      ---------
      x: torch.Tensor
          a tensor of samples of any dimension
      num_timesteps: int
          the total number of timesteps"""
      return torch.randint(num_timesteps, (x.size(0),), device=x.device)
  
imports: from collections import namedtuple
  import torch
  from torch import nn
  from torch.nn import functional
  from tqdm.auto import tqdm
  from speechbrain.utils.data_utils import unsqueeze_as
  from speechbrain.dataio.dataio import length_to_mask
  from speechbrain.utils import data_utils
label_desc: Use image node_id 1 for calling a global function with example usage: sample_timesteps(x, num_timesteps) and returns: torch