code: def interpolate(self, x1, x2, t=None, lam=0.5):
      b, *_, device = *x1.shape, x1.device
      t = default(t, self.num_timesteps - 1)
  
      assert x1.shape == x2.shape
  
      t_batched = torch.stack([torch.tensor(t, device=device)] * b)
      xt1, xt2 = map(lambda x: self.q_sample(x, t=t_batched), (x1, x2))
  
      img = (1 - lam) * xt1 + lam * xt2
      for i in reversed(range(0, t)):
          img = self.p_sample(
              img, torch.full((b,), i, device=device, dtype=torch.long)
          )
  
      return img
  
imports: from functools import partial
  from inspect import isfunction
  import numpy
  import torch
  from torch import nn, einsum
  import torch.nn.functional
label_desc: Use image node_id 11 for calling the GaussianDiffusion obj's underlying member method code with example usage: obj.interpolate(x1, x2, t, lam) and returns: img