code: def _profile_meta(
      target: Callable, *args, **kwargs
  ) -> Tuple[Tuple[Any, ...], GraphInfo]:
      """
      Profile a Callable function with args and kwargs on meta devices.
  
      Args:
          target (Callable): A Callable function
          args (Any): Argument
          kwargs (Any): Argument
  
      Returns:
          out (Tuple[Any, ...]): The argument value that was retrieved.
          meta_info (GraphInfo): The memory cost and FLOPs estimated with `MetaTensor`.
      """
      # This subgraph traces aten level ops inside one node.
      subgraph = Graph()
  
      # `flop_count`` serves as a global dictionary to store results.
      flop_count = {
          Phase.FORWARD: 0,
          Phase.BACKWARD: 0,
      }
  
      # FlopTensor not only get the flop statistics of a single node,
      # it also build a full autograd graph for this node.
      # This makes sure we can analyze the dependencies of memory, and
      # decide which forward intermediate results should be kept until
      # backward is executed.
      # Hopefully, this attempt will provide a better estimation of memory.
      class FlopTensor(MetaTensor):
          _node: Node = None
  
          def __repr__(self):
              if self.grad_fn:
                  return f"FlopTensor({self._tensor}, fake_device='{self.device}', size={tuple(self.shape)}, grad_fn={self.grad_fn})"
              return f"FlopTensor({self._tensor}, fake_device='{self.device}', size={tuple(self.shape)}, requires_grad={self.requires_grad})"
  
          @classmethod
          def __torch_dispatch__(
              cls, func, types, args=(), kwargs=None
          ):
              args_node = tree_map(
                  lambda x: x._node
                  if isinstance(x, FlopTensor)
                  else None,
                  args,
              )
              kwargs_node = tree_map(
                  lambda x: x._node
                  if isinstance(x, FlopTensor)
                  else None,
                  kwargs,
              )
              node = subgraph.create_node(
                  "call_function", func, args_node, kwargs_node
              )
  
              out = super().__torch_dispatch__(
                  func, types, args, kwargs
              )
  
              flop_count[phase] += flop_mapping[func](
                  args, normalize_tuple(out)
              )
              node.meta["phase"] = phase
  
              # super-dainiu: in `nn.MultiheadAttention` this weird thing occurs,
              # i.e. `Phase.PLACEHOLDER` tensors are aliased and saved during
              # `Phase.FORWARD`
              if phase == Phase.FORWARD:
                  if (
                      all(
                          map(
                              partial(
                                  is_phase, phase=Phase.PLACEHOLDER
                              ),
                              node.all_input_nodes,
                          )
                      )
                      and func in ALIAS_ATEN
                  ):
                      node.meta["phase"] = Phase.PLACEHOLDER
  
              # TODO(yby): specify `saved_tensors` for backward memory estimation
              node.meta["saved_tensor"] = []
              if phase == Phase.BACKWARD:
                  node.meta["saved_tensor"] = normalize_tuple(out)
  
              def wrap(x):
                  if isinstance(x, MetaTensor):
                      x = FlopTensor(x)
                      x._node = node
                  return x
  
              out = tree_map(wrap, out)
              return out
  
      def wrap(x):
          if isinstance(x, torch.Tensor):
              x = FlopTensor(x)
              if is_autogradable(x):
                  x.requires_grad_(True)
              x._node = subgraph.create_node(
                  "placeholder",
                  "placeholder",
                  (subgraph._root,),
                  name=subgraph._graph_namespace.create_name(
                      "input", x._tensor
                  ),
              )
              x._node.meta["phase"] = Phase.PLACEHOLDER
              x._node.meta["saved_tensor"] = []
          return x
  
      # Basically, we need to detach the args and kwargs from the outer graph.
      args = tree_map(wrap, args)
      kwargs = tree_map(wrap, kwargs)
  
      def pack(x):
          global cache, do_not_cache
          if (
              isinstance(x, FlopTensor)
              and not x._tensor.data_ptr() in cache
          ):
              tensor = x._tensor.detach()
              tensor.data_ptr = x._tensor.data_ptr
              x._node.meta["saved_tensor"] += [tensor]
              if not do_not_cache:
                  cache.add(x._tensor.data_ptr())
          return x
  
      def unpack(x):
          return x
  
      # `phase` will mark the phase of autograd from outside scope.
      phase = Phase.FORWARD
      # mark saved tensors with saved_tensors_hooks
      with torch.autograd.graph.saved_tensors_hooks(pack, unpack):
          if isinstance(target, str):
              # args[0] is the `self` object for this method call
              self_obj, *args_tail = args
              out = getattr(self_obj, target)(*args_tail, **kwargs)
          else:
              out = target(*args, **kwargs)
  
          # If the output is not a floating point `torch.Tensor` or it does not
          # requires grad, then we should not run backward for this node.
          if all(
              map(
                  lambda x: is_autogradable(x) and x.requires_grad,
                  normalize_tuple(out),
              )
          ):
              grad_out = [
                  torch.zeros_like(t) for t in normalize_tuple(out)
              ]
              phase = Phase.BACKWARD
              torch.autograd.backward(
                  out,
                  grad_out,
              )
  
      graph_info = autograd_graph_analysis(subgraph)
      graph_info.fwd_flop, graph_info.bwd_flop = (
          flop_count[Phase.FORWARD],
          flop_count[Phase.BACKWARD],
      )
  
      def extract_tensor(x: Any):
          if isinstance(x, MetaTensor):
              tensor = x._tensor.detach()
              tensor.data_ptr = x._tensor.data_ptr
              return tensor
          if not isinstance(x, torch.finfo):
              return x
  
      graph_info.fwd_out = list(
          map(extract_tensor, normalize_tuple(out))
      )
  
      def unwrap(x):
          return MetaTensor(x) if isinstance(x, torch.Tensor) else x
  
      return tree_map(unwrap, out), graph_info
  
imports: import time
  from functools import partial
  from typing import Any, Callable, Dict, Tuple
  import torch
  from torch.fx import Graph, Node
  from torch.fx.node import Argument, Target
  from torch.nn.parameter import Parameter
  from torch.utils._pytree import tree_map
  from .._compatibility import compatibility
  from .constants import ALIAS_ATEN, OUTPUT_SAVED_MOD, OUTPUT_SAVED_OPS
  from .dataflow import GraphInfo, Phase, autograd_graph_analysis, is_phase
  from .memory_utils import activation_size, parameter_size
  from .opcount import flop_mapping
  from .tensor import MetaTensor
label_desc: Use image node_id 5 for calling a global function with example usage: _profile_meta(target) and returns: tree_map,  graph_info, x, x, x, unknown, str, self, str, self, str, tuple, str, self, str, out, tensor, x, str, self, str, self, str, tuple, str, self, str, x