code: def value_function():
      config = {
          "in_channels": 14,
          "down_block_types": (
              "DownResnetBlock1D",
              "DownResnetBlock1D",
              "DownResnetBlock1D",
              "DownResnetBlock1D",
          ),
          "up_block_types": (),
          "out_block_type": "ValueFunction",
          "mid_block_type": "ValueFunctionMidBlock1D",
          "block_out_channels": (32, 64, 128, 256),
          "layers_per_block": 1,
          "downsample_each_block": True,
          "sample_size": 65536,
          "out_channels": 14,
          "extra_in_channels": 0,
          "time_embedding_type": "positional",
          "use_timestep_embedding": True,
          "flip_sin_to_cos": False,
          "freq_shift": 1,
          "norm_num_groups": 8,
          "act_fn": "mish",
      }
  
      model = torch.load(
          "/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"
      )
      state_dict = model
      hf_value_function = UNet1DModel(**config)
      print(f"length of state dict: {len(state_dict.keys())}")
      print(
          f"length of value function dict: {len(hf_value_function.state_dict().keys())}"
      )
  
      mapping = dict(
          zip(state_dict.keys(), hf_value_function.state_dict().keys())
      )
      for k, v in mapping.items():
          state_dict[v] = state_dict.pop(k)
  
      hf_value_function.load_state_dict(state_dict)
  
      torch.save(
          hf_value_function.state_dict(),
          "hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin",
      )
      with open(
          "hub/hopper-medium-v2/value_function/config.json", "w"
      ) as f:
          json.dump(config, f)
  
imports: import json
  import os
  import torch
  from diffusers import UNet1DModel
label_desc: Use image node_id 2 for calling a global function with example usage: value_function() without return types