code: def eps_theta(self, x, i, embd, guidance_scale=None):
      with torch.inference_mode():
          noise = self.denoiser(
              x if guidance_scale is None else torch.cat([x] * 2),
              i,
              embd,
          ).sample
          if guidance_scale is not None:
              noise_pred_uncond, noise_pred_text = noise.chunk(2)
              noise = noise_pred_uncond + guidance_scale * (
                  noise_pred_text - noise_pred_uncond
              )
          return noise
  
file_path: diffusion-for-beginners/samplers/exponential_integrator.py
imports: import torch
  import numpy
label_desc: Use image node_id 5 for calling the ExponentialSampler obj's underlying member method code with example usage: obj.eps_theta(x, i, embd, guidance_scale) and returns: noise