code: def sampler_step(
      self, sigma, next_sigma, denoiser, x, cond, uc=None, gamma=0.0
  ):
      sigma_hat = sigma * (gamma + 1.0)
      if gamma > 0:
          eps = torch.randn_like(x) * self.s_noise
          x = (
              x
              + eps
              * append_dims(sigma_hat**2 - sigma**2, x.ndim) ** 0.5
          )
  
      denoised = self.denoise(x, denoiser, sigma_hat, cond, uc)
      d = to_d(x, sigma_hat, denoised)
      dt = append_dims(next_sigma - sigma_hat, x.ndim)
  
      euler_step = self.euler_step(x, d, dt)
      x = self.possible_correction_step(
          euler_step, x, d, dt, next_sigma, denoiser, cond, uc
      )
      return x
  
imports: from typing import Dict, Union
  import torch
  from omegaconf import ListConfig, OmegaConf
  from tqdm import tqdm
  from ...modules.diffusionmodules.sampling_utils import get_ancestral_step, linear_multistep_coeff, to_d, to_neg_log_sigma, to_sigma
  from ...util import append_dims, default, instantiate_from_config
label_desc: Use image node_id 2 for calling the EDMSampler obj's underlying member method code with example usage: obj.sampler_step(sigma, next_sigma, denoiser, x, cond, uc, gamma) and returns: x