class_docstr: A simulation of memory allocation and deallocation in the forward pass
  using ``saved_tensor_hooks``.
  
  Attributes:
      ctx (Dict[int, torch.Tensor]): A dictionary that maps the
          data pointer of a tensor to the tensor itself. This is used
          to track the memory allocation and deallocation.
  
      param_ctx (Dict[int, torch.Tensor]): A dictionary that maps the
          data pointer of all model parameters to the parameter itself.
          This avoids overestimating the memory usage of the intermediate activations.
code: def pack_hook(self, tensor: torch.Tensor):
      if (
          tensor.data_ptr() not in self.param_ctx
          and tensor.data_ptr() not in self.buffer_ctx
      ):
          self.ctx[tensor.data_ptr()] = tensor
      return tensor
  
imports: from typing import Any, Callable, Dict, Optional, Tuple, Union
  import torch
  import torch.fx
  from torch.autograd.graph import saved_tensors_hooks
  from torch.utils._pytree import tree_map
  from colossalai._analyzer._subclasses import MetaTensor, MetaTensorMode
  from colossalai._analyzer.fx.node_util import MetaInfo
  from colossalai.fx._compatibility import compatibility
label_desc: Use image node_id 2 for calling the sim_env obj's underlying member method code with example usage: obj.pack_hook(tensor) and returns: tensor