code: def run_node(self, node: Node):
      self.node_counter += 1
      result = super().run_node(node)
      node.meta["fake_result"] = result
      node.meta["node_idx"] = self.node_counter
  
      # (1) Update metadata with the list of nodes that are used by this node
      # copy_() doesn't read from its first argument; it writes to it, overwriting previous data.
      # We don't want to treat it as "being used as an input".
      node_args = node.args
      if node.target is torch.ops.aten.copy_.default:
          node_args = node_args[1:]
  
      # (2) Update metadata to track aliasing information about view tensor nodes.
      if node.op == "call_function":
          view_type = _get_view_type(node.target)
          if view_type == _ViewType.SingleOutputView:
              assert isinstance(node.args[0], Node)
              node.meta["view_of"] = node.args[0]
          elif view_type == _ViewType.MultiOutputView:
              self.multi_output_view_nodes[node] = node.args[0]
  
          # Check if we returned a multi-output view,
          # and we're now grabbing the individual views from the output.
          #
          # For multi-output views, we want to map each output view to the base,
          # but this mapping involves two separate nodes in FX IR.
          # e.g. "a, b = x_1.split(...)" becomes:
          #    %split_tensor : [num_users=2] = call_function[target=torch.ops.aten.split.Tensor](args = (%x_1, 2), kwargs = {})
          #    %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%split_tensor, 0), kwargs = {})
          #    %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%split_tensor, 1), kwargs = {})
          # And we'd like to set:
          #    getitem1.meta['view_of'] = x_1
          elif node.target is _operator.getitem:
              list_arg = node.args[0]
              maybe_base_of_view = self.multi_output_view_nodes.get(
                  list_arg, None
              )
              if maybe_base_of_view is not None:
                  # Note: we could also track indexing info here for multi-output views.
                  # I don't think this metadata is strictly needed for de-functionalization.
                  assert isinstance(maybe_base_of_view, Node)
                  node.meta["view_of"] = maybe_base_of_view
  
      if "view_of" in node.meta:
          # We're linking the current node with its first argument as views.
          # Assert here that this is actually the case, and their storages are the same.
          assert isinstance(node.meta["fake_result"], FakeTensor)
          assert isinstance(
              node.meta["view_of"].meta["fake_result"], FakeTensor
          )
          view_storage = StorageWeakRef(
              node.meta["fake_result"]._typed_storage()
          )
          base_storage = StorageWeakRef(
              node.meta["view_of"].meta["fake_result"]._typed_storage()
          )
          assert view_storage == base_storage
      return result
  
imports: import torch
  from torch.fx import Node
  from torch.fx._compatibility import compatibility
  from torch._subclasses.fake_tensor import FakeTensorMode, FakeTensor
  from torch.utils._pytree import tree_map_only
  from torch.utils import _pytree
  from torch.multiprocessing.reductions import StorageWeakRef
  import _operator
  from enum import Enum
  import itertools
  from typing import Set, Dict
  from collections import defaultdict
label_desc: Use image node_id 1 for calling the _FunctionalizationMetadataProp obj's underlying member method code with example usage: obj.run_node(node) and returns: result