code: def override_test_values(
      arg_map: Dict[str, str], op_name: str, index: int
  ) -> None:
      assert index == 0 or index == 1
      if op_name == "addr":
          if index == 0:
              arg_map["self"] = "at::rand({6, 6})"
              arg_map["vec1"] = "at::rand({6})"
              arg_map["vec2"] = "at::rand({6})"
          else:
              arg_map["self"] = "at::rand({22, 22})"
              arg_map["vec1"] = "at::rand({22})"
              arg_map["vec2"] = "at::rand({22})"
          return
      if op_name == "mv":
          if index == 0:
              arg_map["self"] = "at::rand({6, 6})"
              arg_map["vec"] = "at::rand({6})"
          else:
              arg_map["self"] = "at::rand({22, 22})"
              arg_map["vec"] = "at::rand({22})"
          return
      if op_name == "addbmm":
          if index == 0:
              arg_map["self"] = "at::rand({6, 6})"
          else:
              arg_map["self"] = "at::rand({22, 22})"
          return
      if op_name == "cross":
          if index == 0:
              arg_map["self"] = "at::rand({3, 3, 3})"
              arg_map["other"] = "at::rand({3, 3, 3})"
          else:
              arg_map["self"] = "at::rand({22, 3, 22})"
              arg_map["other"] = "at::rand({22, 3, 22})"
          return
      if op_name == "take":
          if index == 0:
              arg_map[
                  "index"
              ] = "at::randint(0, 216, {20}, torch::kInt64)"
          else:
              arg_map[
                  "index"
              ] = "at::randint(0, 1000, {100}, torch::kInt64)"
          return
      if op_name == "take_along_dim":
          if index == 0:
              arg_map["indices"] = "at::argsort(self0, 1, true)"
          else:
              arg_map["indices"] = "at::argsort(self1, 1, true)"
          return
      if op_name == "masked_select":
          if index == 0:
              arg_map["mask"] = "at::randn({6, 6, 6}) > 0.5"
          else:
              arg_map["mask"] = "at::rand({22, 22, 22}) > 0.5"
          return
      if op_name == "orgqr":
          if index == 0:
              arg_map["input2"] = "at::rand({6, 6})"
          else:
              arg_map["input2"] = "at::rand({22, 22})"
          return
      if op_name == "ormqr":
          if index == 0:
              arg_map["input2"] = "at::rand({6, 6})"
          else:
              arg_map["input2"] = "at::rand({22, 22})"
          return
      if op_name == "quantile":
          if index == 0:
              arg_map["q"] = "at::rand({6})"
              arg_map["interpolation"] = '"linear"'
          else:
              arg_map["q"] = "at::rand({22})"
              arg_map["interpolation"] = '"linear"'
          return
      if op_name == "nanquantile":
          if index == 0:
              arg_map["q"] = "at::rand({6})"
              arg_map["interpolation"] = '"linear"'
          else:
              arg_map["q"] = "at::rand({22})"
              arg_map["interpolation"] = '"linear"'
          return
      if op_name == "multi_margin_loss":
          if index == 0:
              arg_map["self"] = "at::rand({6, 6})"
              arg_map["target"] = "at::randint(6, {6}, torch::kInt64)"
              arg_map["weight"] = "at::rand({6})"
          else:
              arg_map["self"] = "at::rand({22, 22})"
              arg_map["target"] = "at::randint(22, {22}, torch::kInt64)"
              arg_map["weight"] = "at::rand({22})"
          return
      if op_name == "multilabel_margin_loss":
          if index == 0:
              arg_map["self"] = "at::rand({6, 6})"
              arg_map[
                  "target"
              ] = "at::randint(6, {6, 6}, torch::kInt64)"
          else:
              arg_map["self"] = "at::rand({22, 22})"
              arg_map[
                  "target"
              ] = "at::randint(22, {22, 22}, torch::kInt64)"
          return
      if op_name == "nll_loss":
          if index == 0:
              arg_map["self"] = "at::rand({6, 6})"
              arg_map["target"] = "at::randint(6, {6}, torch::kInt64)"
              arg_map["weight"] = "at::rand({6})"
          else:
              arg_map["self"] = "at::rand({22, 22})"
              arg_map["target"] = "at::randint(22, {22}, torch::kInt64)"
              arg_map["weight"] = "at::rand({22})"
          return
      if op_name == "nll_loss2d":
          if index == 0:
              arg_map["self"] = "at::rand({6, 6, 6, 6})"
              arg_map[
                  "target"
              ] = "at::randint(6, {6, 6, 6}, torch::kInt64)"
              arg_map["weight"] = "at::rand({6})"
          else:
              arg_map["self"] = "at::rand({22, 22, 22, 22})"
              arg_map[
                  "target"
              ] = "at::randint(22, {22, 22, 22}, torch::kInt64)"
              arg_map["weight"] = "at::rand({22})"
          return
      if op_name in (
          "fft_fft",
          "fft_ifft",
          "fft_rfft",
          "fft_irfft",
          "fft_hfft",
          "fft_ihfft",
      ):
          arg_map["norm"] = '"forward"'
          return
      if op_name == "linalg_tensorinv":
          if index == 0:
              arg_map["self"] = "at::rand({6, 6, 6, 6})"
              arg_map["ind"] = "2"
          else:
              arg_map["self"] = "at::rand({22, 22, 22, 22})"
              arg_map["ind"] = "2"
          return
      if op_name == "addmv":
          if index == 0:
              arg_map["self"] = "at::rand({2})"
              arg_map["mat"] = "at::rand({2, 2})"
              arg_map["vec"] = "at::rand({2})"
          else:
              arg_map["self"] = "at::rand({35})"
              arg_map["mat"] = "at::rand({35, 35})"
              arg_map["vec"] = "at::rand({35})"
          return
      if op_name == "acosh":
          if index == 0:
              arg_map[
                  "self"
              ] = "at::rand({2, 2, 2}) + at::ones({2, 2, 2})"
          else:
              arg_map[
                  "self"
              ] = "at::rand({5, 5, 5}) + at::ones({5, 5, 5})"
          return
      if op_name == "adaptive_max_pool2d_backward":
          if index == 0:
              arg_map["grad_output"] = "at::rand({2, 2, 2}, at::kFloat)"
              arg_map["self"] = "at::rand({2, 2, 2}, at::kFloat)"
              arg_map[
                  "indices"
              ] = "at::randint(0, 1, {2, 2, 2}, at::kLong)"
          else:
              arg_map["grad_output"] = "at::rand({3, 3, 3}, at::kFloat)"
              arg_map["self"] = "at::rand({3, 3, 3}, at::kFloat)"
              arg_map[
                  "indices"
              ] = "at::randint(0, 1, {3, 3, 3}, at::kLong)"
          return
      if op_name == "adaptive_max_pool3d_backward":
          if index == 0:
              arg_map[
                  "grad_output"
              ] = "at::rand({2, 2, 2, 2}, at::kFloat)"
              arg_map["self"] = "at::rand({2, 2, 2, 2}, at::kFloat)"
              arg_map[
                  "indices"
              ] = "at::randint(0, 1, {2, 2, 2, 2}, at::kLong)"
          else:
              arg_map[
                  "grad_output"
              ] = "at::rand({3, 3, 3, 3}, at::kFloat)"
              arg_map["self"] = "at::rand({3, 3, 3, 3}, at::kFloat)"
              arg_map[
                  "indices"
              ] = "at::randint(0, 1, {3, 3, 3, 3}, at::kLong)"
          return
      if op_name == "bitwise_left_shift":
          if index == 0:
              arg_map[
                  "self"
              ] = "at::randint(1, 1 << 4, {6, 6, 6}, at::kInt)"
              arg_map[
                  "other"
              ] = "at::randint(1, 26, {6, 6, 6}, at::kInt)"
          else:
              arg_map[
                  "self"
              ] = "at::randint(1, 1 << 4, {22, 22, 22}, at::kInt)"
              arg_map[
                  "other"
              ] = "at::randint(1, 26, {22, 22, 22}, at::kInt)"
          return
      if op_name == "bitwise_right_shift":
          if index == 0:
              arg_map[
                  "self"
              ] = "at::randint(1 << 21, 1 << 30, {6, 6, 6}, at::kInt)"
              arg_map[
                  "other"
              ] = "at::randint(1, 22, {6, 6, 6}, at::kInt)"
          else:
              arg_map[
                  "self"
              ] = "at::randint(1 << 21, 1 << 30, {22, 22, 22}, at::kInt)"
              arg_map[
                  "other"
              ] = "at::randint(1, 22, {22, 22, 22}, at::kInt)"
          return
      if op_name == "gather":
          if index == 0:
              arg_map["self"] = "at::randint(1, 100, {2,2,2}, at::kInt)"
              arg_map["dim"] = "1"
              arg_map[
                  "index"
              ] = "at::randint(0, 1, {2,2,2}, torch::kInt64)"
              arg_map["sparse_grad"] = "false"
          else:
              arg_map["self"] = "at::randint(1, 100, {5,5,5}, at::kInt)"
              arg_map["dim"] = "1"
              arg_map[
                  "index"
              ] = "at::randint(0, 4, {5,5,5}, torch::kInt64)"
              arg_map["sparse_grad"] = "false"
          return
      if op_name == "gelu":
          if index == 0:
              arg_map["self"] = "at::rand({6, 6, 6})"
              arg_map["approximate"] = '"tanh"'
          else:
              arg_map["self"] = "at::rand({22, 22, 22})"
              arg_map["approximate"] = '"tanh"'
          return
      if op_name == "gelu_backward":
          if index == 0:
              arg_map["grad_output"] = "at::rand({6, 6, 6})"
              arg_map["self"] = "at::rand({6, 6, 6})"
              arg_map["approximate"] = '"tanh"'
          else:
              arg_map["grad_output"] = "at::rand({22, 22, 22})"
              arg_map["self"] = "at::rand({22, 22, 22})"
              arg_map["approximate"] = '"tanh"'
          return
      if op_name == "index_add":
          if index == 0:
              arg_map["self"] = "at::rand({2})"
              arg_map["dim"] = "0"
              arg_map["index"] = "at::randint(0, 1, {2}, at::kInt)"
              arg_map["source"] = "at::rand({2})"
              arg_map["alpha"] = "2"
          else:
              arg_map["self"] = "at::rand({16})"
              arg_map["dim"] = "0"
              arg_map["index"] = "at::randint(0, 10, {16}, at::kInt)"
              arg_map["source"] = "at::rand({16})"
              arg_map["alpha"] = "2"
          return
      if op_name == "index_copy":
          if index == 0:
              arg_map["self"] = "at::rand({2})"
              arg_map["dim"] = "0"
              arg_map["index"] = "at::randint(0, 1, {2}, at::kLong)"
              arg_map["source"] = "at::rand({2})"
          else:
              arg_map["self"] = "at::rand({32})"
              arg_map["dim"] = "0"
              arg_map["index"] = "at::randint(0, 10, {32}, at::kLong)"
              arg_map["source"] = "at::rand({32})"
          return
      if op_name == "linalg_cross":
          if index == 0:
              arg_map["self"] = "at::rand({6, 3, 6})"
              arg_map["other"] = "at::rand({6, 3, 6})"
              arg_map["dim"] = "1"
          else:
              arg_map["self"] = "at::rand({22, 3, 22})"
              arg_map["other"] = "at::rand({22, 3, 22})"
              arg_map["dim"] = "1"
          return
      if op_name == "nll_loss_backward":
          if index == 0:
              arg_map["grad_output"] = "at::rand({})"
              arg_map["self"] = "at::rand({6})"
              arg_map[
                  "target"
              ] = "at::randint(0, 5, {6}, torch::kInt64)"
              arg_map["weight"] = "at::rand({6})"
              arg_map["reduction"] = "1"
              arg_map["ignore_index"] = "1"
              arg_map["total_weight"] = "at::rand({})"
          else:
              arg_map["grad_output"] = "at::rand({})"
              arg_map["self"] = "at::rand({36})"
              arg_map[
                  "target"
              ] = "at::randint(0, 11, {36}, torch::kInt64)"
              arg_map["weight"] = "at::rand({36})"
              arg_map["reduction"] = "1"
              arg_map["ignore_index"] = "1"
              arg_map["total_weight"] = "at::rand({})"
          return
      if op_name in ["scatter", "scatter_add", "_scatter_reduce"]:
          if index == 0:
              arg_map[
                  "self"
              ] = "at::randint(1, 100, {2,2,2}, torch::kInt64)"
              arg_map[
                  "index"
              ] = "at::randint(0, 1, {2,2,2}, torch::kInt64)"
              arg_map[
                  "src"
              ] = "at::randint(1, 100, {2,2,2}, torch::kInt64)"
          else:
              arg_map[
                  "self"
              ] = "at::randint(1, 100, {5,5,5}, torch::kInt64)"
              arg_map[
                  "index"
              ] = "at::randint(0, 1, {5,5,5}, torch::kInt64)"
              arg_map[
                  "src"
              ] = "at::randint(1, 100, {5,5,5}, torch::kInt64)"
          if "reduce" in arg_map:
              arg_map["reduce"] = (
                  '"sum"' if op_name == "_scatter_reduce" else '"add"'
              )
          return
      if op_name == "scatter_reduce":
          arg_map["reduce"] = '"mean"'
          if index == 0:
              arg_map[
                  "index"
              ] = "at::randint(6, {6, 6, 6}, torch::kInt64)"
          else:
              arg_map[
                  "index"
              ] = "at::randint(22, {22, 22, 22}, torch::kInt64)"
          return
      if op_name == "special_zeta":
          if index == 0:
              arg_map[
                  "self"
              ] = "at::rand({2,2,2}, at::kDouble) + at::ones({2,2,2})"
              arg_map[
                  "other"
              ] = "at::rand({2,2,2}, at::kDouble) + at::ones({2,2,2})"
          else:
              arg_map[
                  "self"
              ] = "at::rand({5,5,5}, at::kDouble) + at::ones({5,5,5})"
              arg_map[
                  "other"
              ] = "at::rand({5,5,5}, at::kDouble) + at::ones({5,5,5})"
          return
      if op_name == "_convert_indices_from_csr_to_coo":
          if index == 0:
              arg_map[
                  "crow_indices"
              ] = "torch::tensor({1}, torch::kInt32)"
              arg_map[
                  "col_indices"
              ] = "torch::tensor({0, 1, 0}, torch::kInt32)"
              arg_map["out_int32"] = "false"
          else:
              arg_map[
                  "crow_indices"
              ] = "torch::tensor({0}, torch::kInt32)"
              arg_map[
                  "col_indices"
              ] = "torch::tensor({0, 1, 0, 2, 1, 2, 0, 1, 0, 2, 1, 2}, torch::kInt32)"
              arg_map["out_int32"] = "false"
          return
      if op_name == "_convert_indices_from_coo_to_csr":
          if index == 0:
              arg_map["self"] = "at::randint(0, 3, {2}, at::kInt)"
              arg_map["size"] = "10"
              arg_map["out_int32"] = "false"
          else:
              arg_map["self"] = "at::randint(0, 3, {12}, at::kInt)"
              arg_map["size"] = "24"
              arg_map["out_int32"] = "false"
          return
      if op_name in ("diagonal", "linalg_diagonal"):
          arg_map["offset"] = "0"
          arg_map["dim0"] = "1"
          arg_map["dim1"] = "2"
          return
  
imports: from typing import Dict, Union
  from torchgen.model import NativeFunctionsGroup, NativeFunctionsViewGroup
label_desc: Use image node_id 3 for calling a global function with example usage: override_test_values(arg_map, op_name, index) without return types