code: def flatten_check(tensor_type, start_dim, end_dim):
      l = len(tensor_type.__args__)
  
      start_dim = l if start_dim == -1 else abs(start_dim)
      end_dim = l + end_dim + 1 if end_dim < 0 else end_dim + 1
  
      if (
          0 <= start_dim <= (l - 1)
          and 0 <= end_dim <= l
          and start_dim < end_dim
      ):
          my_args = list(tensor_type.__args__)
          lhs = my_args[0:start_dim]
          rhs = my_args[end_dim:]
          mid = my_args[start_dim:end_dim]
          if Dyn in mid:
              mid = [Dyn]
          else:
              mid = [reduce(operator.mul, my_args[start_dim:end_dim])]
          new_type_list = lhs + mid + rhs
          return TensorType(tuple(new_type_list))
      else:
          raise TypeError(
              f"Incompatible dimensions {start_dim}, {end_dim - 1} in type {tensor_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 21 for calling a global function with example usage: flatten_check(tensor_type, start_dim, end_dim) and returns: TensorType