code: def prepare_inputs(
      self, x: torch.Tensor, s: torch.Tensor, c: dict, uc: dict
  ) -> Tuple[torch.Tensor, torch.Tensor, dict]:
      c_out = dict()
  
      for k in c:
          if (
              k
              in ["vector", "crossattn", "concat"]
              + self.additional_cond_keys
          ):
              c_out[k] = torch.cat((uc[k], c[k]), 0)
          else:
              assert c[k] == uc[k]
              c_out[k] = c[k]
      return torch.cat([x] * 2), torch.cat([s] * 2), c_out
  
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 3 for calling the LinearPredictionGuider obj's underlying member method code with example usage: obj.prepare_inputs(x, s, c, uc) and returns: torch,  torch,  c_out