code: def prepare_extra_step_kwargs(self, generator, eta):
      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
      # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
      # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
      # and should be between [0, 1]
  
      accepts_eta = "eta" in set(
          inspect.signature(self.scheduler.step).parameters.keys()
      )
      extra_step_kwargs = {}
      if accepts_eta:
          extra_step_kwargs["eta"] = eta
  
      # check if the scheduler accepts generator
      accepts_generator = "generator" in set(
          inspect.signature(self.scheduler.step).parameters.keys()
      )
      if accepts_generator:
          extra_step_kwargs["generator"] = generator
      return extra_step_kwargs
  
imports: import inspect
  from typing import Any, Dict, List, Optional, Union
  import torch
  import torch.nn
  from transformers import AutoModel, AutoTokenizer, CLIPImageProcessor
  from diffusers import DiffusionPipeline
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.loaders import LoraLoaderMixin
  from diffusers.models import AutoencoderKL, UNet2DConditionModel
  from diffusers.models.lora import adjust_lora_scale_text_encoder
  from diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
  from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
  from diffusers.schedulers import KarrasDiffusionSchedulers
  from diffusers.utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
  from diffusers.utils.torch_utils import randn_tensor
label_desc: Use image node_id 10 for calling the GlueGenStableDiffusionPipeline obj's underlying member method code with example usage: obj.prepare_extra_step_kwargs(generator, eta) and returns: extra_step_kwargs