code: def calc_last_two_dims(constraint, d: List[DVar]):
      """
      Generates constraints for the last two dimensions of a convolution or a maxpool output
      Args:
          constraint: CalcConv or CalcMaxPool
          d: The list of output dimensions
  
      Returns: Constraints for calculating the last two dimensions of the output
  
      """
  
      assert isinstance(constraint, (CalcConv, CalcMaxPool))
  
      b3 = constraint.matching_constraint[2]
      b4 = constraint.matching_constraint[3]
  
      b3_dyn = Conj(
          [
              BinConstraintD(d[2], Dyn, op_eq),
              BinConstraintD(b3, Dyn, op_eq),
          ]
      )
      b4_dyn = Conj(
          [
              BinConstraintD(d[3], Dyn, op_eq),
              BinConstraintD(b4, Dyn, op_eq),
          ]
      )
  
      d3_not_dyn = Conj(
          [
              BinConstraintD(d[2], Dyn, op_neq),
              BinConstraintD(b3, Dyn, op_neq),
          ]
      )
      d4_not_dyn = Conj(
          [
              BinConstraintD(d[3], Dyn, op_neq),
              BinConstraintD(b4, Dyn, op_neq),
          ]
      )
  
      # transform parameters into tuples incase they are not already
      padding = (
          (constraint.padding, constraint.padding)
          if isinstance(constraint.padding, int)
          else constraint.padding
      )
      kernel = (
          (constraint.kernel, constraint.kernel)
          if isinstance(constraint.kernel, int)
          else constraint.kernel
      )
      stride = (
          (constraint.stride, constraint.stride)
          if isinstance(constraint.stride, int)
          else constraint.stride
      )
      dilation = (
          (constraint.dilation, constraint.dilation)
          if isinstance(constraint.dilation, int)
          else constraint.dilation
      )
  
      f1 = BinConstraintD(
          b3, BinConstraintD(2, padding[0], op_mul), op_add
      )
      f2 = BinConstraintD(
          dilation[0], BinConstraintD(kernel[0], 1, op_sub), op_mul
      )
      f3 = BinConstraintD(
          BinConstraintD(BinConstraintD(f1, f2, op_sub), 1, op_sub),
          stride[0],
          op_div,
      )
      f4 = BinConstraintD(f3, 1, op_add)
  
      c4 = Disj(
          [b3_dyn, Conj([d3_not_dyn, BinConstraintD(d[2], f4, op_eq)])]
      )
  
      f11 = BinConstraintD(
          b4, BinConstraintD(2, padding[1], op_mul), op_add
      )
      f22 = BinConstraintD(
          dilation[1], BinConstraintD(kernel[1], 1, op_sub), op_mul
      )
      f33 = BinConstraintD(
          BinConstraintD(BinConstraintD(f11, f22, op_sub), 1, op_sub),
          stride[1],
          op_div,
      )
      f44 = BinConstraintD(f33, 1, op_add)
  
      c5 = Disj(
          [b4_dyn, Conj([d4_not_dyn, BinConstraintD(d[3], f44, op_eq)])]
      )
  
      return c4, c5
  
imports: import copy
  import itertools
  from torch.fx.experimental.migrate_gradual_types.constraint_generator import BinConstraintT, MAX_TENSOR_RANK
  from torch.fx.experimental.migrate_gradual_types.constraint import T, BinConstraintD, Conj, Constraint, DVar, TVar, Transpose
  from torch.fx.experimental.migrate_gradual_types.constraint import Disj, TGreatestUpperBound
  from torch.fx.experimental.migrate_gradual_types.constraint import DGreatestUpperBound
  from torch.fx.experimental.migrate_gradual_types.constraint import CalcConv, CalcMaxPool
  from torch.fx.experimental.migrate_gradual_types.constraint import CalcProduct, CanReshape
  from torch.fx.experimental.migrate_gradual_types.constraint import ApplyBroadcasting, Prod, F, GetItem, GetItemTensor, IndexSelect
  from torch.fx.experimental.migrate_gradual_types.operation import op_eq, op_precision, op_leq, op_matching
  from torch.fx.experimental.migrate_gradual_types.operation import op_consistency, op_neq
  from torch.fx.experimental.migrate_gradual_types.operation import op_mul, op_add, op_sub, op_div, op_mod
  from torch.fx.experimental.migrate_gradual_types.util import gen_tensor_dims, gen_nat_constraints, gen_dvar
  from torch.fx.tensor_type import TensorType, Dyn
  from typing import Callable, Dict, List
label_desc: Use image node_id 20 for calling a global function with example usage: calc_last_two_dims(constraint, d) and returns: c4,  c5