class_docstr: The variance exploding stochastic differential equation (SDE) scheduler.
  
  :param snr: coefficient weighting the step from the model_output sample (from the network) to the random noise.
  :param sigma_min: initial noise scale for sigma sequence in sampling procedure. The minimum sigma should mirror the
          distribution of the data.
  :param sigma_max: :param sampling_eps: the end value of sampling, where timesteps decrease progessively from 1 to
  epsilon. :param correct_steps: number of correction steps performed on a produced sample. :param tensor_format:
  "np" or "pd" for the expected format of samples passed to the Scheduler.
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code: def set_timesteps(self, num_inference_steps, sampling_eps=None):
      sampling_eps = (
          sampling_eps
          if sampling_eps is not None
          else self.config.sampling_eps
      )
      tensor_format = getattr(self, "tensor_format", "pd")
      if tensor_format == "np":
          self.timesteps = np.linspace(
              1, sampling_eps, num_inference_steps
          )
      elif tensor_format == "pd":
          self.timesteps = paddle.linspace(
              1, sampling_eps, num_inference_steps
          )
      else:
          raise ValueError(
              f"`self.tensor_format`: {self.tensor_format} is not valid."
          )
  
file_path: PaddleHub/modules/image/text_to_image/stable_diffusion_waifu/diffusers/schedulers/scheduling_sde_ve.py
imports: from typing import Union
  import numpy
  import paddle
  from ..configuration_utils import ConfigMixin
  from ..configuration_utils import register_to_config
  from .scheduling_utils import SchedulerMixin
label_desc: Use image node_id 2 for calling the ScoreSdeVeScheduler obj's underlying member method code with example usage: obj.set_timesteps(num_inference_steps, sampling_eps) without return types