PROCESS_DUP_HANDLE                = 0x0040
	PROCESS_QUERY_INFORMATION         = 0x0400
	PROCESS_QUERY_LIMITED_INFORMATION = 0x1000
	PROCESS_SET_INFORMATION           = 0x0200
	PROCESS_SET_QUOTA                 = 0x0100
	PROCESS_SUSPEND_RESUME            = 0x0800
	PROCESS_TERMINATE                 = 0x0001
	PROCESS_VM_OPERATION              = 0x0008
	PROCESS_VM_READ                   = 0x0010
	PROCESS_VM_WRITE                  = 0x0020

	{0x018F, 0x018F, d{0, 202, 0}},
	{0x0190, 0x0190, d{0, 203, 0}},
	{0x0191, 0x0192, d{UpperLower, UpperLower, UpperLower}},
	{0x0193, 0x0193, d{0, 205, 0}},
	{0x0194, 0x0194, d{0, 207, 0}},
	{0x0195, 0x0195, d{97, 0, 97}},
	{0x0196, 0x0196, d{0, 211, 0}},
	{0x0197, 0x0197, d{0, 209, 0}},
	{0x0198, 0x0199, d{UpperLower, UpperLower, UpperLower}},
	{0x019A, 0x019A, d{163, 0, 163}},
	{0x019C, 0x019C, d{0, 211, 0}},

      def __init__(self, bias: Optional[core.Tensor]):
        self._bias = bias
        self._kernel = np.random.uniform(size=y_shape).astype('f4')
        self._min = (-0.8, -0.8, -0.9)
        self._max = (0.9, 0.9, 1.0)

      @def_function.function(
          input_signature=[
              tensor_spec.TensorSpec(
                  name='x', shape=shapes[0], dtype=dtypes.float32
              )
          ]
      )

    : TFL_Op<"bucketize", [Pure, SameOperandsAndResultShape]> {
  let summary = "Bucketizes 'input' based on 'boundaries'.";

  let description = [{
Example:

If the inputs are `boundaries = [0, 10, 100]` and
`input = [[-5, 10000][150, 10][5, 100]]`,
then the output will be `output = [[0, 3][3, 2][1, 3]]`.
  }];

  let arguments = (ins
    TFL_TensorOf<[F32, F64, I32, I64]>:$input,