code: def sample_midpoint_ancestral(
      model, x, ts, generator, progress=False, callback=None
  ):
      """Ancestral sampling with midpoint method steps."""
      s_in = x.new_ones([x.shape[0]])
      step_size = 1 / len(ts)
      if progress:
          from tqdm.auto import tqdm
  
          ts = tqdm(ts)
  
      for tn in ts:
          dn = model(x, tn * s_in)
          dn_2 = model(
              x + (step_size / 2) * dn, (tn + step_size / 2) * s_in
          )
          x = x + step_size * dn_2
          if callback is not None:
              callback({"x": x, "tn": tn, "dn": dn, "dn_2": dn_2})
      return x
  
imports: import random
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from piq import LPIPS
  from torchvision.transforms import RandomCrop
  from .None import dist_util
  from .nn import mean_flat, append_dims, append_zero
  from .random_util import get_generator
label_desc: Use image node_id 7 for calling a global function with example usage: sample_midpoint_ancestral(model, x, ts, generator, progress, callback) and returns: x