class_docstr: Scheduler with Heun steps for discrete beta schedules.
  
  This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
  methods the library implements for all schedulers such as loading and saving.
  
  Args:
      num_train_timesteps (`int`, defaults to 1000):
          The number of diffusion steps to train the model.
      beta_start (`float`, defaults to 0.0001):
          The starting `beta` value of inference.
      beta_end (`float`, defaults to 0.02):
          The final `beta` value.
      beta_schedule (`str`, defaults to `"linear"`):
          The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
          `linear` or `scaled_linear`.
      trained_betas (`np.ndarray`, *optional*):
          Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
      prediction_type (`str`, defaults to `epsilon`, *optional*):
          Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
          `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
          Video](https://imagen.research.google/video/paper.pdf) paper).
      clip_sample (`bool`, defaults to `True`):
          Clip the predicted sample for numerical stability.
      clip_sample_range (`float`, defaults to 1.0):
          The maximum magnitude for sample clipping. Valid only when `clip_sample=True`.
      use_karras_sigmas (`bool`, *optional*, defaults to `False`):
          Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`,
          the sigmas are determined according to a sequence of noise levels {σi}.
      timestep_spacing (`str`, defaults to `"linspace"`):
          The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
          Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
      steps_offset (`int`, defaults to 0):
          An offset added to the inference steps. You can use a combination of `offset=1` and
          `set_alpha_to_one=False` to make the last step use step 0 for the previous alpha product like in Stable
          Diffusion.
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docstr: The index counter for current timestep. It will increae 1 after each scheduler step.
docstr_tok: ["The","index","counter","for","current","timestep",".","It","will","increae","1","after","each","scheduler","step","."]
code: def step_index(self):
      """
      The index counter for current timestep. It will increae 1 after each scheduler step.
      """
      return self._step_index
  
imports: import math
  from collections import defaultdict
  from typing import List, Optional, Tuple, Union
  import numpy
  import torch
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
label_desc: Use image node_id 4 for calling the HeunDiscreteScheduler obj's underlying member method code with example usage: obj.step_index() and returns: self