code: def parse_txt_array(
      src: List[str],
      sep: Optional[str] = None,
      start: int = 0,
      end: Optional[int] = None,
      dtype: Optional[torch.dtype] = None,
      device: Optional[torch.device] = None,
  ) -> Tensor:
      empty = torch.empty(0, dtype=dtype)
      to_number = float if empty.is_floating_point() else int
  
      return torch.tensor(
          [
              [to_number(x) for x in line.split(sep)[start:end]]
              for line in src
          ],
          dtype=dtype,
      ).squeeze()
  
imports: from typing import List, Optional
  import fsspec
  import torch
  from torch import Tensor
label_desc: Use image node_id 1 for calling a global function with example usage: parse_txt_array(src, sep, start, end, dtype, device) and returns: torch