code: def getitem_inference_rule(n: Node, symbols, constraints, counter):
      assert isinstance(n.args[0], Node)
  
      # dimension output case
      if isinstance(n.args[1], int):
          # create and store the new dimension variable
          get_item_output, counter = gen_dvar(counter)
          symbols[n] = get_item_output
  
          # retrieve arg variables
          get_item_arg = symbols[n.args[0]]
          assert isinstance(get_item_arg, TVar)
  
          # if the input is dynamic, we accept any index and return
          # a dynamic dimension as output
          input_dyn = BinConstraintT(get_item_arg, Dyn, op_eq)
          output_dyn = BinConstraintD(get_item_output, Dyn, op_eq)
          c1 = Conj([input_dyn, output_dyn])
  
          # if the input is a tensor,
          # generate a getItem constraint which will be expanded based on the
          # tensor dimension.
  
          c2 = [
              GetItem(i + 1, n.args[1], get_item_output, get_item_arg)
              for i in range(MAX_TENSOR_RANK)
          ]
  
          # since the output is a dimension, we make sure it's a natural number
          # added as a conjunction to the disjunction of c2
          c3 = BinConstraintD(0, get_item_output, op_leq)
          return [Disj([c1, Conj([Disj(c2), c3])])], counter
  
      # tensor output case
      elif isinstance(n.args[1], tuple):
          # create and store the new tensor variable
          get_item_output, counter = gen_tvar(counter)
          symbols[n] = get_item_output
  
          # retrieve arg variables
          if n.args[0] in symbols:
              get_item_arg = symbols[n.args[0]]
              assert isinstance(get_item_arg, TVar)
  
              input_dyn = BinConstraintT(get_item_arg, Dyn, op_eq)
              output_dyn = BinConstraintT(get_item_output, Dyn, op_eq)  # type: ignore[assignment]
              c1 = Conj([input_dyn, output_dyn])
  
              c2 = [
                  GetItemTensor(i + 1, n.args[1], get_item_output, get_item_arg)  # type: ignore[misc]
                  for i in range(MAX_TENSOR_RANK)
              ]
          else:
              # TODO: we should figure out why there is a key-error here.
              return [], counter
  
          return [Disj([c1, *c2])], counter
  
      else:
          raise RuntimeError("Method not yet implemented")
  
imports: import torch
  import operator
  import warnings
  from typing import Callable, Dict, Iterable
  from torch.fx._symbolic_trace import _assert_is_none
  from torch.fx.experimental.migrate_gradual_types.constraint import ApplyBroadcasting, CalcProduct, Disj, TGreatestUpperBound, CalcMaxPool, CalcConv, Conj, BinConstraintT, CanReshape, BinConstraintD, GetItem, T, F, TVar, DVar, GetItemTensor, IndexSelect, Transpose, DGreatestUpperBound
  from torch.fx.experimental.migrate_gradual_types.operation import op_eq, op_matching, op_consistency, op_leq, op_precision, op_gt, op_div, op_sub, op_neq, op_lt, op_add, op_mul
  from torch.fx.node import Target, Node
  from torch.fx.experimental.migrate_gradual_types.util import gen_tensor_dims, gen_nat_constraints, gen_dvar, gen_tvar, gen_bvar
  from torch.fx.tensor_type import Dyn, TensorType
  from torch.nn.modules.conv import Conv2d
  from torch.nn.modules.batchnorm import BatchNorm2d
label_desc: Use image node_id 20 for calling a global function with example usage: getitem_inference_rule(n, symbols, constraints, counter) and returns: list,  counter, list,  counter, list,  counter