code: def segment(
      x: torch.tensor,
      segment_indices: torch.tensor,
      segment_size=4,
      pad_short=False,
  ):
      """Segment each sample in a batch based on the provided segment indices
  
      Args:
          x (torch.tensor): Input tensor.
          segment_indices (torch.tensor): Segment indices.
          segment_size (int): Expected output segment size.
          pad_short (bool): Pad the end of input tensor with zeros if shorter than the segment size.
      """
      # pad the input tensor if it is shorter than the segment size
      if pad_short and x.shape[-1] < segment_size:
          x = torch.nn.functional.pad(x, (0, segment_size - x.size(2)))
  
      segments = torch.zeros_like(x[:, :, :segment_size])
  
      for i in range(x.size(0)):
          index_start = segment_indices[i]
          index_end = index_start + segment_size
          x_i = x[i]
          if pad_short and index_end >= x.size(2):
              # pad the sample if it is shorter than the segment size
              x_i = torch.nn.functional.pad(
                  x_i, (0, (index_end + 1) - x.size(2))
              )
          segments[i] = x_i[:, index_start:index_end]
      return segments
  
imports: import numpy
  import torch
  from scipy.stats import betabinom
  from torch.nn import functional
label_desc: Use image node_id 2 for calling a global function with example usage: segment(x, segment_indices, segment_size, pad_short) and returns: segments