code: def test_simple_1d(self, device, dtypes):
      val_dtype, length_type = dtypes
      lengths = [1, 2, 3, 0]
      data = [1, float("nan"), 3, 4, 5, 5]
  
      for reduction in reductions:
          for initial in [0, None]:
              check_backward = True if initial is not None else False
              initial_value = initial
              default_value = get_default_value(
                  initial_value, reduction
              )
              if reduction == "max":
                  expected_result = [1, float("nan"), 5, default_value]
                  expected_grad = [1, 1, 0, 0, 0.5, 0.5]
              elif reduction == "mean":
                  expected_result = [
                      1,
                      float("nan"),
                      4.666,
                      default_value,
                  ]
                  expected_grad = [1.0, 0.5, 0.5, 0.333, 0.333, 0.333]
              elif reduction == "min":
                  if initial is not None:
                      initial_value = 1000  # some high number
                      default_value = get_default_value(
                          initial_value, reduction
                      )
                  expected_result = [1, float("nan"), 4, default_value]
                  expected_grad = [1.0, 1.0, 0, 1, 0, 0]
              elif reduction == "sum":
                  expected_result = [1, float("nan"), 14, default_value]
                  expected_grad = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
              elif reduction == "prod":
                  if initial is not None:
                      initial_value = 2  # 0 initial_value will zero out everything for prod
                      default_value = get_default_value(
                          initial_value, reduction
                      )
                      expected_result = [
                          2,
                          float("nan"),
                          200,
                          default_value,
                      ]
                      expected_grad = [
                          2.0,
                          6.0,
                          float("nan"),
                          50.0,
                          40.0,
                          40.0,
                      ]
                  else:
                      expected_result = [
                          1,
                          float("nan"),
                          100,
                          default_value,
                      ]
                      expected_grad = [
                          1.0,
                          3.0,
                          float("nan"),
                          25.0,
                          20.0,
                          20.0,
                      ]
              for axis in [0, -1]:
                  for unsafe in [True, False]:
                      self._test_common(
                          reduction,
                          device,
                          val_dtype,
                          unsafe,
                          axis,
                          initial_value,
                          data,
                          lengths,
                          expected_result,
                          expected_grad,
                          check_backward,
                          length_type,
                      )
  
file_path: pytorch/test/test_segment_reductions.py
imports: from itertools import product
  from functools import partial
  import numpy
  import torch
  from torch.testing._internal.common_device_type import instantiate_device_type_tests, dtypes
  from torch.testing._internal.common_utils import TestCase, run_tests, gradcheck, parametrize, skipIfRocm
label_desc: Use image node_id 2 for calling the TestSegmentReductions obj's underlying member method code with example usage: obj.test_simple_1d(device, dtypes) without return types