code: def get_sigma_gen(self, num_sigmas):
      sigma_generator = range(num_sigmas - 1)
      if self.verbose:
          print("#" * 30, " Sampling setting ", "#" * 30)
          print(f"Sampler: {self.__class__.__name__}")
          print(
              f"Discretization: {self.discretization.__class__.__name__}"
          )
          print(f"Guider: {self.guider.__class__.__name__}")
          sigma_generator = tqdm(
              sigma_generator,
              total=num_sigmas,
              desc=f"Sampling with {self.__class__.__name__} for {num_sigmas} steps",
          )
      return sigma_generator
  
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 4 for calling the BaseDiffusionSampler obj's underlying member method code with example usage: obj.get_sigma_gen(num_sigmas) and returns: sigma_generator