code: def flatten_refinement_rule(n: Node):
      """
      Generates equality constraints between the dimensions of the input and output
      that will not be involved in the flatten operation
      """
      assert isinstance(n.args[0], Node)
  
      eq_const = []
  
      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 isinstance(n.type, TensorType) and isinstance(
          n.args[0].type, TensorType
      ):
          l = len(n.type.__args__)
          arg_type = n.args[0].type
          start_dim = l if start_dim == -1 else start_dim
          end_dim = l + end_dim + 1 if end_dim < 0 else end_dim + 1
  
          for t1, t2 in zip(
              n.type.__args__[0:start_dim],
              arg_type.__args__[0:start_dim],
          ):
              eq_const.append(Equality(t1, t2))
  
          for t1, t2 in zip(
              n.type.__args__[end_dim:], arg_type.__args__[end_dim:]
          ):
              eq_const.append(Equality(t1, t2))
      return eq_const
  
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 28 for calling a global function with example usage: flatten_refinement_rule(n) and returns: eq_const