code: def linspace(
      backcast_length: int, forecast_length: int, centered: bool = False
  ) -> Tuple[np.ndarray, np.ndarray]:
      if centered:
          norm = max(backcast_length, forecast_length)
          start = -backcast_length
          stop = forecast_length - 1
      else:
          norm = backcast_length + forecast_length
          start = 0
          stop = backcast_length + forecast_length - 1
      lin_space = np.linspace(
          start / norm,
          stop / norm,
          backcast_length + forecast_length,
          dtype=np.float32,
      )
      b_ls = lin_space[:backcast_length]
      f_ls = lin_space[backcast_length:]
      return b_ls, f_ls
  
imports: from typing import Tuple
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling a global function with example usage: linspace(backcast_length, forecast_length, centered) and returns: b_ls,  f_ls