code: def __call__(
      self, x: torch.Tensor, sigma: torch.Tensor
  ) -> torch.Tensor:
      x_u, x_c = x.chunk(2)
  
      x_u = rearrange(x_u, "(b t) ... -> b t ...", t=self.num_frames)
      x_c = rearrange(x_c, "(b t) ... -> b t ...", t=self.num_frames)
      scale = repeat(self.scale, "1 t -> b t", b=x_u.shape[0])
      scale = append_dims(scale, x_u.ndim).to(x_u.device)
  
      return rearrange(
          x_u + scale * (x_c - x_u), "b t ... -> (b t) ..."
      )
  
imports: import logging
  from abc import ABC, abstractmethod
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  from einops import rearrange, repeat
  from ...util import append_dims, default
label_desc: Use image node_id 2 for calling the LinearPredictionGuider obj's underlying member method code with example usage: obj.__call__(x, sigma) and returns: rearrange