class_docstr: _FakePalettizationTensorHook is the class to assist in using CPU when we only want to utilize a certain percentage
  of the GPU memory.
docstr: Method to pack reused variables on to CPU.
docstr_tok: ["Method","to","pack","reused","variables","on","to","CPU","."]
code: def reuse_pack(self, x: _torch.Tensor):
      """
      Method to pack reused variables on to CPU.
      """
      if x.layout != _torch.sparse_coo and x.size() in self.size_list:
          idx = self.size_list.index(x.size())
  
          assert self.size_list[idx] is not None
  
          header = self.tensor_list[idx][0].to(self.device_list[idx])
  
          if _torch.equal(x[0], -header * header / self.palett_tau):
              return idx, "x_c_dist"
          elif _torch.equal(
              x[0], _F.softmax(-header * header / self.palett_tau)
          ):
              return idx, "softmax"
          else:
              return x.to_sparse(), "sparse"
  
      return x
  
file_path: coremltools/coremltools/optimize/torch/palettization/_fake_palettizer_tensor_hook.py
imports: import torch
  import torch.nn.functional
label_desc: Use image node_id 4 for calling the _FakePalettizationTensorHook obj's underlying member method code with example usage: obj.reuse_pack(x) and returns: x, idx,  str, idx,  str, x,  str