code: def maximum_path_cython(value, mask):
      """Cython optimised version.
      Shapes:
          - value: :math:`[B, T_en, T_de]`
          - mask: :math:`[B, T_en, T_de]`
      """
      value = value * mask
      device = value.device
      dtype = value.dtype
      value = value.data.cpu().numpy().astype(np.float32)
      path = np.zeros_like(value).astype(np.int32)
      mask = mask.data.cpu().numpy()
  
      t_x_max = mask.sum(1)[:, 0].astype(np.int32)
      t_y_max = mask.sum(2)[:, 0].astype(np.int32)
      maximum_path_c(path, value, t_x_max, t_y_max)
      return torch.from_numpy(path).to(device=device, dtype=dtype)
  
imports: import numpy
  import torch
  from scipy.stats import betabinom
  from torch.nn import functional
label_desc: Use image node_id 8 for calling a global function with example usage: maximum_path_cython(value, mask) and returns: torch