class_docstr: `DDIMInverseScheduler` is the reverse scheduler of [`DDIMScheduler`].
  
  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`, `scaled_linear`, or `squaredcos_cap_v2`.
      trained_betas (`np.ndarray`, *optional*):
          Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
      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`.
      set_alpha_to_one (`bool`, defaults to `True`):
          Each diffusion step uses the alphas product value at that step and at the previous one. For the final step
          there is no previous alpha. When this option is `True` the previous alpha product is fixed to 0, otherwise
          it uses the alpha value at step `num_train_timesteps - 1`.
      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 `num_train_timesteps - 1` for the previous alpha
          product.
      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).
      timestep_spacing (`str`, defaults to `"leading"`):
          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.
      rescale_betas_zero_snr (`bool`, defaults to `False`):
          Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and
          dark samples instead of limiting it to samples with medium brightness. Loosely related to
          [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).
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docstr: Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
  current timestep.
  
  Args:
      sample (`torch.FloatTensor`):
          The input sample.
      timestep (`int`, *optional*):
          The current timestep in the diffusion chain.
  
  Returns:
      `torch.FloatTensor`:
          A scaled input sample.
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code: def scale_model_input(
      self, sample: torch.FloatTensor, timestep: Optional[int] = None
  ) -> torch.FloatTensor:
      """
      Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
      current timestep.
  
      Args:
          sample (`torch.FloatTensor`):
              The input sample.
          timestep (`int`, *optional*):
              The current timestep in the diffusion chain.
  
      Returns:
          `torch.FloatTensor`:
              A scaled input sample.
      """
      return sample
  
file_path: llmga/llmga/diffusers/src/diffusers/schedulers/scheduling_ddim_inverse.py
imports: import math
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  import numpy
  import torch
  from diffusers.configuration_utils import ConfigMixin, register_to_config
  from diffusers.schedulers.scheduling_utils import SchedulerMixin
  from diffusers.utils import BaseOutput, deprecate
label_desc: Use image node_id 2 for calling the DDIMInverseScheduler obj's underlying member method code with example usage: obj.scale_model_input(sample, timestep) and returns: sample