code: def create_motion_field_and_warp_latents(
      motion_field_strength_x,
      motion_field_strength_y,
      frame_ids,
      latents,
  ):
      """
      Creates translation motion and warps the latents accordingly
  
      Args:
          motion_field_strength_x: motion strength along x-axis
          motion_field_strength_y: motion strength along y-axis
          frame_ids: indexes of the frames the latents of which are being processed.
              This is needed when we perform chunk-by-chunk inference
          latents: latent codes of frames
  
      Returns:
          warped_latents: warped latents
      """
      motion_field = create_motion_field(
          motion_field_strength_x=motion_field_strength_x,
          motion_field_strength_y=motion_field_strength_y,
          frame_ids=frame_ids,
          device=latents.device,
          dtype=latents.dtype,
      )
      warped_latents = latents.clone().detach()
      for i in range(len(warped_latents)):
          warped_latents[i] = warp_single_latent(
              latents[i][None], motion_field[i][None]
          )
      return warped_latents
  
imports: import copy
  from dataclasses import dataclass
  from typing import Callable, List, Optional, Union
  import numpy
  import PIL.Image
  import torch
  import torch.nn.functional
  from torch.nn.functional import grid_sample
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
  from diffusers.models import AutoencoderKL, UNet2DConditionModel
  from diffusers.pipelines.stable_diffusion import StableDiffusionPipeline, StableDiffusionSafetyChecker
  from diffusers.schedulers import KarrasDiffusionSchedulers
  from diffusers.utils import BaseOutput
label_desc: Use image node_id 8 for calling a global function with example usage: create_motion_field_and_warp_latents(motion_field_strength_x, motion_field_strength_y, frame_ids, latents) and returns: warped_latents