docstr: Type check a given fx node.
  Current operations:
  - Reshape
  - Transpose
  - Add
  - Relu
  - conv2d
  - batchnorm2d
  - flatten
  - maxpool2d
  - adaptiveavgpool2d
  - linear
code: def type_check_node(self, n: Node):
      """
      Type check a given fx node.
      Current operations:
      - Reshape
      - Transpose
      - Add
      - Relu
      - conv2d
      - batchnorm2d
      - flatten
      - maxpool2d
      - adaptiveavgpool2d
      - linear
      """
      if n.type is None:
          n.type = Dyn
  
      if n.op == "placeholder":
          return n.type
  
      elif n.op == "get_attr":
          t = get_parameter(self.traced, n.target)  # type: ignore[arg-type]
          if isinstance(t.data, torch.Tensor):
              n.type = TensorType(t.data.shape)
          return n.type
  
      elif n.op == "call_function":
          if n.target == getattr:
              assert getattr in _INFERENCE_RULES
              return _INFERENCE_RULES[n.target](n, self.traced)
  
          elif n.target in _INFERENCE_RULES:
              return _INFERENCE_RULES[n.target](n)
          else:
              raise RuntimeError(
                  f"No inference rule registered for target {n.target}!"
              )
  
      elif n.op == "call_module":
          module_instance = self.traced.get_submodule(n.target)
          if type(module_instance) in _INFERENCE_RULES:
              return _INFERENCE_RULES[type(module_instance)](
                  n, module_instance
              )
          else:
              raise RuntimeError(
                  f"No inference rule registered for class {type(module_instance)}!"
              )
  
      elif n.op == "output":
  
          def get_node_type(a):
              return a.type
  
          n.type = torch.fx.node.map_arg(n.args[0], get_node_type)
          return n.type
  
      else:
          raise NotImplementedError(
              f"Method {n.op} not yet implemented"
          )
  
imports: from functools import reduce
  import torch
  import operator
  from torch.fx.tensor_type import Dyn, is_consistent, TensorType, is_more_precise
  from typing import Callable, Dict
  from torch.fx.node import Target, Node
  from torch.nn.modules.batchnorm import BatchNorm2d
  from torch.nn.modules.conv import Conv2d
  from torch.fx.experimental.refinement_types import Equality
  import itertools
  from torch.fx.experimental.unification import Var
  import sympy
label_desc: Use image node_id 3 for calling the GraphTypeChecker obj's underlying member method code with example usage: obj.type_check_node(n) and returns: n, n, _INFERENCE_RULES, _INFERENCE_RULES, _INFERENCE_RULES, n, a