code: def test_index_ind_dtype(self):
      x = Tensor.randn(4, 4)
      # ind_long = torch.randint(4, (4,), dtype=torch.long)
      # TODO should we spend an extra line to allow for randint other dtypes?
      # copied from randint
      ind_long = (
          Tensor.rand(
              (4,),
          )
          * (4 - 0)
          + 0
      ).cast(dtypes.int64)
      # ind_int = ind_long.int()
      ind_int = (ind_long).cast(dtypes.int32)
      ref = x[ind_long, ind_long]
      res = x[ind_int, ind_int]
      numpy_testing_assert_equal_helper(ref, res)
      ref = x[ind_long, :]
      res = x[ind_int, :]
      numpy_testing_assert_equal_helper(ref, res)
      ref = x[:, ind_long]
      res = x[:, ind_int]
      numpy_testing_assert_equal_helper(ref, res)
      # no repeating indices for index_put
      # TODO setitem
      """
      src = Tensor.randn(4)
      ind_long = Tensor.arange(4, dtype=dtypes.int64)
      ind_int = ind_long.cast(dtypes.int32)
      for accum in (True, False):
        inp_ref = clone(x)
        inp_res = clone(x)
        torch.index_put_(inp_ref, (ind_long, ind_long), src, accum)
        torch.index_put_(inp_res, (ind_int, ind_int), src, accum)
        numpy_testing_assert_equal_helper(inp_ref, inp_res)
      """
  
imports: import math, unittest, random, copy
  import numpy
  from tinygrad import Tensor, dtypes, Device
  from tinygrad.lazy import LazyBuffer
  from tinygrad.shape.shapetracker import ShapeTracker
  from tinygrad.shape.view import View
  from tinygrad.helpers import CI
label_desc: Use image node_id 9 for calling the TestIndexing obj's underlying member method code with example usage: obj.test_index_ind_dtype() without return types