code: def _inversion_transform(self, image, target_size=512):
      from torchvision import transforms
  
      tform = transforms.Compose(
          [
              transforms.Resize(target_size),
              transforms.CenterCrop(target_size),
              transforms.ToTensor(),
          ]
      )
      image = tform(image).unsqueeze(0).to(self.device)
      return 2.0 * image - 1.0
  
imports: import logging
  import os
  import torch
  import torch.nn.functional
  import tqdm
  from diffusers import AutoencoderKL, ControlNetModel, DDPMScheduler, DDIMScheduler, PNDMScheduler, UNet2DConditionModel
  from torch import nn
  from transformers import CLIPTokenizer
  from transformers.activations import QuickGELUActivation
  from lavis.common.registry import registry
  from lavis.common.utils import download_and_untar, is_url
  from lavis.models.base_model import BaseModel
  from lavis.models.blip2_models.blip2_qformer import Blip2Qformer
  from lavis.models.blip_diffusion_models.modeling_ctx_clip import CtxCLIPTextModel
  from lavis.models.blip_diffusion_models.utils import numpy_to_pil, prepare_cond_image
  from lavis.models.blip_diffusion_models.ptp_utils import LocalBlend, P2PCrossAttnProcessor, AttentionRefine
label_desc: Use image node_id 14 for calling the BlipDiffusion obj's underlying member method code with example usage: obj._inversion_transform(image, target_size) and returns: unknown