code: def rand_segments(
      x: torch.tensor,
      x_lengths: torch.tensor = None,
      segment_size=4,
      let_short_samples=False,
      pad_short=False,
  ):
      """Create random segments based on the input lengths.
  
      Args:
          x (torch.tensor): Input tensor.
          x_lengths (torch.tensor): Input lengths.
          segment_size (int): Expected output segment size.
          let_short_samples (bool): Allow shorter samples than the segment size.
          pad_short (bool): Pad the end of input tensor with zeros if shorter than the segment size.
  
      Shapes:
          - x: :math:`[B, C, T]`
          - x_lengths: :math:`[B]`
      """
      _x_lenghts = x_lengths.clone()
      B, _, T = x.size()
      if pad_short:
          if T < segment_size:
              x = torch.nn.functional.pad(x, (0, segment_size - T))
              T = segment_size
      if _x_lenghts is None:
          _x_lenghts = T
      len_diff = _x_lenghts - segment_size
      if let_short_samples:
          _x_lenghts[len_diff < 0] = segment_size
          len_diff = _x_lenghts - segment_size
      else:
          assert all(
              len_diff > 0
          ), f" [!] At least one sample is shorter than the segment size ({segment_size}). \n {_x_lenghts}"
      segment_indices = (
          torch.rand([B]).type_as(x) * (len_diff + 1)
      ).long()
      ret = segment(
          x, segment_indices, segment_size, pad_short=pad_short
      )
      return ret, segment_indices
  
imports: import numpy
  import torch
  from scipy.stats import betabinom
  from torch.nn import functional
label_desc: Use image node_id 3 for calling a global function with example usage: rand_segments(x, x_lengths, segment_size, let_short_samples, pad_short) and returns: ret,  segment_indices