code: def flatten_inference_rule(n: Node):
      """
      Applies the flatten shape information to the input then gets the
      greatest upper bound of the resulting type and the existing type
      """
      assert isinstance(n.args[0], Node)
  
      # set the default start and end dims
      start_dim = 1
      end_dim = -1
  
      if len(n.args) > 1:
          assert isinstance(n.args[1], int)
          start_dim = n.args[1]
  
      if len(n.args) > 2:
          assert isinstance(n.args[2], int)
          end_dim = n.args[2]
  
      if n.args[0].type == Dyn and isinstance(n.type, TensorType):
          n.args[0].type = expand_to_tensor_dim(
              n.args[0].type, len(n.type.__args__)
          )
  
      if isinstance(n.args[0].type, TensorType):
          output_type = flatten_check(
              n.args[0].type, start_dim, end_dim
          )
          n.type = get_greatest_upper_bound(output_type, n.type)
  
      return n.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 22 for calling a global function with example usage: flatten_inference_rule(n) and returns: n