code: def expand_to_tensor_dim(t, n):
      """
      Expand a type to the desired tensor dimension if possible
      Raise an error otherwise.
      - t is the given type
      - n is a number of dimensions to expand to
      """
      if t == Dyn:
          dims = [Dyn] * n
          return TensorType(tuple(dims))
      elif isinstance(t, TensorType):
          if len(t.__args__) != n:
              raise TypeError(
                  f"Cannot extend tensor. Tensor {t} has rank {len(t.__args__)}. It should have rank {n}"
              )
          return t
      else:
          raise TypeError(f"Cannot match the type {t}")
  
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 1 for calling a global function with example usage: expand_to_tensor_dim(t, n) and returns: TensorType, t