code: def rearrange_0(tensor, f):
      F, C, H, W = tensor.size()
      tensor = torch.permute(
          torch.reshape(tensor, (F // f, f, C, H, W)), (0, 2, 1, 3, 4)
      )
      return tensor
  
imports: import copy
  import inspect
  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 ...image_processor import VaeImageProcessor
  from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
  from ...models import AutoencoderKL, UNet2DConditionModel
  from ...models.lora import adjust_lora_scale_text_encoder
  from ...schedulers import KarrasDiffusionSchedulers
  from ...utils import USE_PEFT_BACKEND, BaseOutput, logging, scale_lora_layers, unscale_lora_layers
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline
  from ..stable_diffusion import StableDiffusionSafetyChecker
label_desc: Use image node_id 1 for calling a global function with example usage: rearrange_0(tensor, f) and returns: tensor