code: def transpose_inference_rule(n: Node):
      """
      We check that dimensions for the transpose operations
      are within range of the tensor type of the node
      """
      if n.target == torch.transpose:
          assert isinstance(n.args[0], Node)
          t = n.args[0].type
  
          assert isinstance(n.args[1], int)
          assert isinstance(n.args[2], int)
          dim1, dim2 = n.args[1], n.args[2]
  
          if t == Dyn:
              n.type = Dyn
              return n.type
  
          elif isinstance(t, TensorType):
              if 0 <= dim1 < len(t.__args__) and 0 <= dim2 < len(
                  t.__args__
              ):
                  new_type = list(t.__args__)
                  new_type[dim1], new_type[dim2] = (
                      new_type[dim2],
                      new_type[dim1],
                  )
                  final = TensorType(new_type)
                  n.type = get_greatest_upper_bound(n.type, final)
                  return n.type
              else:
                  raise TypeError(
                      f"Cannot transpose {dim1} and {dim2} in type {t} for node {n}"
                  )
          else:
              raise TypeError(
                  f"Cannot transpose {dim1} and {dim2} in type {t} for node {n}"
              )
  
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 8 for calling a global function with example usage: transpose_inference_rule(n) and returns: n, n