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 Callable, Dict, List, Optional, Union
  import numpy
  import PIL
  import PIL.Image
  import torch
  from transformers import T5EncoderModel, T5Tokenizer
  from ...loaders import LoraLoaderMixin
  from ...models import Kandinsky3UNet, VQModel
  from ...schedulers import DDPMScheduler
  from ...utils import deprecate, is_accelerate_available, logging, replace_example_docstring
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
label_desc: Use image node_id 7 for calling the Kandinsky3Img2ImgPipeline obj's underlying member method code with example usage: obj.prepare_extra_step_kwargs(generator, eta) and returns: extra_step_kwargs