code: def convert_efficientformer_checkpoint(
      checkpoint_path: Path,
      efficientformer_config_file: Path,
      pytorch_dump_path: Path,
      push_to_hub: bool,
  ):
      orig_state_dict = torch.load(checkpoint_path, map_location="cpu")[
          "model"
      ]
      config = EfficientFormerConfig.from_json_file(
          efficientformer_config_file
      )
      model = EfficientFormerForImageClassificationWithTeacher(config)
      model_name = "_".join(
          checkpoint_path.split("/")[-1].split(".")[0].split("_")[:-1]
      )
  
      num_meta4D_last_stage = (
          config.depths[-1] - config.num_meta3d_blocks + 1
      )
      new_state_dict = convert_torch_checkpoint(
          orig_state_dict, num_meta4D_last_stage
      )
  
      model.load_state_dict(new_state_dict)
      model.eval()
  
      pillow_resamplings = {
          "bilinear": PILImageResampling.BILINEAR,
          "bicubic": PILImageResampling.BICUBIC,
          "nearest": PILImageResampling.NEAREST,
      }
  
      # prepare image
      image = prepare_img()
      image_size = 256
      crop_size = 224
      processor = EfficientFormerImageProcessor(
          size={"shortest_edge": image_size},
          crop_size={"height": crop_size, "width": crop_size},
          resample=pillow_resamplings["bicubic"],
      )
      pixel_values = processor(
          images=image, return_tensors="pt"
      ).pixel_values
  
      # original processing pipeline
      image_transforms = Compose(
          [
              Resize(
                  image_size,
                  interpolation=pillow_resamplings["bicubic"],
              ),
              CenterCrop(crop_size),
              ToTensor(),
              Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD),
          ]
      )
      original_pixel_values = image_transforms(image).unsqueeze(0)
  
      assert torch.allclose(original_pixel_values, pixel_values)
  
      outputs = model(pixel_values)
      logits = outputs.logits
  
      expected_shape = (1, 1000)
  
      if "l1" in model_name:
          expected_logits = torch.Tensor(
              [
                  -0.1312,
                  0.4353,
                  -1.0499,
                  -0.5124,
                  0.4183,
                  -0.6793,
                  -1.3777,
                  -0.0893,
                  -0.7358,
                  -2.4328,
              ]
          )
          assert torch.allclose(
              logits[0, :10], expected_logits, atol=1e-3
          )
          assert logits.shape == expected_shape
      elif "l3" in model_name:
          expected_logits = torch.Tensor(
              [
                  -1.3150,
                  -1.5456,
                  -1.2556,
                  -0.8496,
                  -0.7127,
                  -0.7897,
                  -0.9728,
                  -0.3052,
                  0.3751,
                  -0.3127,
              ]
          )
          assert torch.allclose(
              logits[0, :10], expected_logits, atol=1e-3
          )
          assert logits.shape == expected_shape
      elif "l7" in model_name:
          expected_logits = torch.Tensor(
              [
                  -1.0283,
                  -1.4131,
                  -0.5644,
                  -1.3115,
                  -0.5785,
                  -1.2049,
                  -0.7528,
                  0.1992,
                  -0.3822,
                  -0.0878,
              ]
          )
          assert logits.shape == expected_shape
      else:
          raise ValueError(
              f"Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"
          )
  
      # Save Checkpoints
      Path(pytorch_dump_path).mkdir(exist_ok=True)
      model.save_pretrained(pytorch_dump_path)
      print(
          f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"
      )
      processor.save_pretrained(pytorch_dump_path)
      print(f"Processor successfuly saved at {pytorch_dump_path}")
  
      if push_to_hub:
          print("Pushing model to the hub...")
  
          model.push_to_hub(
              repo_id=f"Bearnardd/{pytorch_dump_path}",
              commit_message="Add model",
              use_temp_dir=True,
          )
          processor.push_to_hub(
              repo_id=f"Bearnardd/{pytorch_dump_path}",
              commit_message="Add image processor",
              use_temp_dir=True,
          )
  
file_path: transformers/src/transformers/models/efficientformer/convert_efficientformer_original_pytorch_checkpoint_to_pytorch.py
imports: import argparse
  import re
  from pathlib import Path
  import requests
  import torch
  from PIL import Image
  from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
  from transformers import EfficientFormerConfig, EfficientFormerForImageClassificationWithTeacher, EfficientFormerImageProcessor
  from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
label_desc: Use image node_id 4 for calling a global function with example usage: convert_efficientformer_checkpoint(checkpoint_path, efficientformer_config_file, pytorch_dump_path, push_to_hub) without return types