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 that initialises packing and saving values to CPU.
docstr_tok: ["Method","that","initialises","packing","and","saving","values","to","CPU","."]
code: def init_pack(self, x: _torch.Tensor):
      """
      Method that initialises packing and saving values to CPU.
      """
      if x.size() in self.size_list:
          idx = self.size_list.index(x.size())
  
          if self.tensor_list[idx] is None:
              self.device_list[idx] = x.device
  
              if self.use_cpu:
                  self.tensor_list[idx] = _torch.empty(
                      x.size(),
                      dtype=x.dtype,
                      layout=x.layout,
                      pin_memory=True,
                  )
                  self.tensor_list[idx].copy_(x)
              else:
                  self.tensor_list[idx] = x
  
          elif _torch.equal(
              self.tensor_list[idx][0].to(self.device_list[idx]), x[0]
          ):
              pass
          else:
              assert False
  
          return idx
  
      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 2 for calling the _FakePalettizationTensorHook obj's underlying member method code with example usage: obj.init_pack(x) and returns: x, idx