Tilize/untilize compute kernels are buggy

[nardoTT]

Describe the bug
The following tilize/untilize compute kernels seem to be buggy for uint32 data type:

• tilize_block in tt_metal/include/compute_kernel_api/tilize.h
• untilize_block in tt_metal/include/compute_kernel_api/untilize.h

To Reproduce
The following tests use the kernels above and fail with assertion error:

@pytest.mark.parametrize("shape", [[32, 288]])
def test_untilize_with_unpadding_uint32(shape, device):
    torch.manual_seed(2005)
    input_a = torch.randint(1, 64, shape, dtype=torch.int32)
    input_tensor = ttnn.from_torch(input_a, device=device, layout=ttnn.TILE_LAYOUT, dtype=ttnn.uint32)
    output_tensor = ttnn.untilize_with_unpadding(input_tensor, [3,279])
    output_tensor = ttnn.to_torch(output_tensor)
    assert_with_pcc(input_a[:4, :280], output_tensor)

@pytest.mark.parametrize("shape", [[32, 512]])
def test_untilize_uint32(shape, device):
    torch.manual_seed(2005)
    input_a = torch.randint(1, 64, shape, dtype=torch.int32)
    input_tensor = ttnn.from_torch(input_a, device=device, layout=ttnn.TILE_LAYOUT, dtype=ttnn.uint32)
    output_tensor = ttnn.untilize(input_tensor)
    output_tensor = ttnn.to_torch(output_tensor)
    assert_with_pcc(input_a, output_tensor)

@pytest.mark.parametrize("shape", [[15, 15]])
def test_tilize_with_val_padding_uint32(shape, device):
    torch.manual_seed(2005)
    input_a = torch.randint(0, 64, shape, dtype=torch.int32)
    input_tensor = ttnn.from_torch(input_a, device=device, layout=ttnn.ROW_MAJOR_LAYOUT, dtype=ttnn.uint32)
    output_tensor = ttnn.tilize_with_val_padding(input_tensor, [32,32], 70)
    output_tensor = ttnn.to_torch(output_tensor)
    assert_with_pcc(input_a, output_tensor)


@pytest.mark.parametrize("shape", [[32, 32]])
def test_tilize_uint32(shape, device):
    torch.manual_seed(2005)
    input_a = torch.randint(0, 64, shape, dtype=torch.int32)
    input_tensor = ttnn.from_torch(input_a, device=device, layout=ttnn.ROW_MAJOR_LAYOUT, dtype=ttnn.uint32)
    output_tensor = ttnn.tilize(input_tensor)
    output_tensor = ttnn.to_torch(output_tensor)
    assert_with_pcc(input_a, output_tensor)

Additional context
Note: You need to add DataType::UINT32 to this line to test uint32 type in tilize

tt-metal/ttnn/cpp/ttnn/operations/data_movement/tilize/device/tilize_op.cpp

Line 25 in af79262

input_tensor_a.get_dtype() == DataType::BFLOAT16 or input_tensor_a.get_dtype() == DataType::FLOAT32,