code: def reshape_inference_rule(n: Node):
      """
      Without dynamism, the rule checks that the
      product of the elements of the argument tensor
      type is equal to the product of the elements
      of the required shape. We gradualize this rule
      by adding a case to handle fully dynamic input
      as well as input where some of the tensor dimensions
      are unknown. In this case we check for divisibility
      """
      assert isinstance(n.args[0], Node)
      t1 = n.args[0].type
  
      assert isinstance(n.args[1], list)
      t2 = n.args[1]
      t2_type = TensorType([Dyn if elem == -1 else elem for elem in t2])
  
      # if we do not know the original tensor dimension,
      # we return the required dimension
      if t1 == Dyn:
          n.type = t2_type
          return t2_type
  
      # if any of the dimensions are unknown,
      # we check for divisibility
      elif isinstance(t1, TensorType):
          assert isinstance(t1, TensorType)
          a = [e if e != Dyn else 1 for e in t1.__args__]
          p1 = reduce(operator.mul, a)
          p2 = reduce(operator.mul, t2)
          if p1 % p2 == 0 or p2 % p1 == 0:
              n.type = t2_type
              return t2_type
          else:
              raise TypeError(
                  f"Cannot reshape in node {n} from {t1} to {t2_type}"
              )
      else:
          raise TypeError(
              f"Cannot reshape in node {n} from {t1} to {t2_type}"
          )
  
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 9 for calling a global function with example usage: reshape_inference_rule(n) and returns: t2_type, t2_type