class_docstr: A fourth-order Improved Pseudo Linear Multistep scheduler.
  
  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.
      trained_betas (`np.ndarray`, *optional*):
          Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
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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.
  
  Returns:
      `torch.FloatTensor`:
          A scaled input sample.
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code: def scale_model_input(
      self, sample: torch.FloatTensor, *args, **kwargs
  ) -> 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.
  
      Returns:
          `torch.FloatTensor`:
              A scaled input sample.
      """
      return sample
  
file_path: llmga/llmga/diffusers/src/diffusers/schedulers/scheduling_ipndm.py
imports: import math
  from typing import List, Optional, Tuple, Union
  import numpy
  import torch
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils import SchedulerMixin, SchedulerOutput
label_desc: Use image node_id 6 for calling the IPNDMScheduler obj's underlying member method code with example usage: obj.scale_model_input(sample) and returns: sample