class_docstr: Log-linear homotopy schedule.
docstr: Initialize LogLinearHomotopySchedule.
  
  Args:
      start: start value of homotopy
      end: end value of homotopy
      num_steps: number of steps in the homotopy schedule.
docstr_tok: ["Initialize","LogLinearHomotopySchedule",".","Args",":","start",":","start","value","of","homotopy","end",":","end","value","of","homotopy","num_steps",":","number","of","steps","in","the","homotopy","schedule","."]
code: def __init__(self, start: float, end: float, num_steps: int):
      r"""Initialize LogLinearHomotopySchedule.
  
      Args:
          start: start value of homotopy
          end: end value of homotopy
          num_steps: number of steps in the homotopy schedule.
      """
      super().__init__(
          values=torch.logspace(
              math.log10(start),
              math.log10(end),
              num_steps,
              dtype=torch.double,
          ).tolist()
      )
  
imports: from __future__ import annotations
  import math
  from abc import ABC, abstractmethod
  from dataclasses import dataclass
  from typing import Any, Callable, List, Optional, Union
  import torch
  from torch import Tensor
  from torch.nn import Parameter
label_desc: Use image node_id 1 to create a new LogLinearHomotopySchedule object from inherited base classes: FixedHomotopySchedule with example: obj = LogLinearHomotopySchedule(start, end, num_steps)