code: def create_diffusers_schedular(original_config):
      schedular = DDIMScheduler(
          num_train_timesteps=original_config["model"]["params"][
              "timesteps"
          ],
          beta_start=original_config["model"]["params"]["linear_start"],
          beta_end=original_config["model"]["params"]["linear_end"],
          beta_schedule="scaled_linear",
      )
      return schedular
  
imports: import argparse
  import re
  from typing import List, Union
  import torch
  import yaml
  from transformers import AutoFeatureExtractor, AutoTokenizer, ClapConfig, ClapModel, GPT2Config, GPT2Model, SpeechT5HifiGan, SpeechT5HifiGanConfig, T5Config, T5EncoderModel
  from diffusers import AudioLDM2Pipeline, AudioLDM2ProjectionModel, AudioLDM2UNet2DConditionModel, AutoencoderKL, DDIMScheduler, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, HeunDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler
  from diffusers.utils import is_safetensors_available
  from diffusers.utils.import_utils import BACKENDS_MAPPING
label_desc: Use image node_id 10 for calling a global function with example usage: create_diffusers_schedular(original_config) and returns: schedular