class_docstr: This is here to replace torch.randn_like of k-diffusion.
  
  k-diffusion has random_sampler argument for most samplers, but not for all, so
  this is needed to properly replace every use of torch.randn_like.
  
  We need to replace to make images generated in batches to be same as images generated individually.
code: def randn_like(self, x):
      return self.rng.next()
  
imports: import inspect
  from collections import namedtuple
  import numpy
  import torch
  from PIL import Image
  from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared, sd_models
  from modules.shared import opts, state
  import k_diffusion.sampling
label_desc: Use image node_id 3 for calling the TorchHijack obj's underlying member method code with example usage: obj.randn_like(x) and returns: self