code: def get_smld_sigma_schedule(
      sigma_min: float,
      sigma_max: float,
      num_scales: int,
      dtype: Optional[torch.dtype] = None,
      device: Optional[torch.device] = None,
  ) -> Tensor:
      r"""Generates a set of noise values on a logarithmic scale for "Score
      Matching with Langevin Dynamics" from the `"Generative Modeling by
      Estimating Gradients of the Data Distribution"
      <https://arxiv.org/abs/1907.05600>`_ paper.
  
      This function returns a vector of sigma values that define the schedule of
      noise levels used during Score Matching with Langevin Dynamics.
      The sigma values are determined on a logarithmic scale from
      :obj:`sigma_max` to :obj:`sigma_min`, inclusive.
  
      Args:
          sigma_min (float): The minimum value of sigma, corresponding to the
              lowest noise level.
          sigma_max (float): The maximum value of sigma, corresponding to the
              highest noise level.
          num_scales (int): The number of sigma values to generate, defining the
              granularity of the noise schedule.
          dtype (torch.dtype, optional): The output data type.
              (default: :obj:`None`)
          device (torch.device, optional): The output device.
              (default: :obj:`None`)
      """
      return torch.linspace(
          math.log(sigma_max),
          math.log(sigma_min),
          num_scales,
          dtype=dtype,
          device=device,
      ).exp()
  
imports: import math
  from typing import Literal, Optional
  import torch
  from torch import Tensor
label_desc: Use image node_id 1 for calling a global function with example usage: get_smld_sigma_schedule(sigma_min, sigma_max, num_scales, dtype, device) and returns: torch