code: def patch_float(model: nn.Module):
      float_holder = torch.jit.trace(
          lambda: torch.ones([]).float(), example_inputs=[]
      )
      float_input = list(
          float_holder.graph.findNode("aten::to").inputs()
      )[1]
      float_node = float_input.node()
  
      def patch_flt(module):
          try:
              graphs = (
                  [module.graph] if hasattr(module, "graph") else []
              )
          except RuntimeError:
              graphs = []
  
          if hasattr(module, "forward1"):
              graphs.append(module.forward1.graph)
  
          for graph in graphs:
              for node in graph.findAllNodes("aten::to"):
                  inputs = list(node.inputs())
                  for i in [
                      1,
                      2,
                  ]:  # dtype can be the second or third argument to aten::to()
                      if inputs[i].node()["value"] == 5:
                          inputs[i].node().copyAttributes(float_node)
  
      model.apply(patch_flt)
      patch_flt(model.encode_image)
      patch_flt(model.encode_text)
  
      model.float()
  
file_path: towhee/towhee/models/clip/clip_utils.py
imports: from typing import Union, List, Dict
  from pkg_resources import packaging
  from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
  from torchvision.transforms import InterpolationMode
  import torch
  import torchvision
  from torch import nn
  from .simple_tokenizer import SimpleTokenizer
label_desc: Use image node_id 9 for calling a global function with example usage: patch_float(model) without return types