ResizeCropMirror seems ignoring output_dtype option.

It reproduces on DALI 0.11.0/CUDA 10.1/CentOS 7.5 with the following code:

import nvidia.dali as dali
import numpy as np


class ResizeTo1x1Pipeline(dali.pipeline.Pipeline):
    def __init__(self):
        super(ResizeTo1x1Pipeline, self).__init__(batch_size=1, num_threads=1, device_id=0)
        self._fetcher = dali.ops.ExternalSource()
        # FastResizeCropMirror has the same problem
        self._resizer = dali.ops.ResizeCropMirror(
            resize_x=1, resize_y=1, crop=[1, 1],
            interp_type=dali.types.DALIInterpType.INTERP_LINEAR,
            output_dtype=dali.types.DALIDataType.FLOAT)

    def define_graph(self):
        self._images = self._fetcher()
        return self._resizer(self._images)

    def set_input_image(self, image):
        self.feed_input(self._images, image[np.newaxis])


print('DALI: {}'.format(dali.__version__))
pipeline = ResizeTo1x1Pipeline()
pipeline.build()

# a grayscale 3x2 image ([[1,2,3],[4,5,6]])
source_image = np.arange(6, dtype=np.uint8).reshape(2, 3, 1)
pipeline.set_input_image(source_image)
    
resized_images, = pipeline.run()

actual_image = resized_images.at(0)
expected_image = source_image.mean(axis=(0, 1), keepdims=True, dtype=np.float32)
np.testing.assert_allclose(actual_image, expected_image)

On my machine, DALI returns UINT8 instead of FLOAT as follows:

DALI: 0.11.0
Traceback (most recent call last):
  File "foo.py", line 35, in <module>
    np.testing.assert_allclose(actual_image, expected_image)
  File "/home/XXXXXXXX/miniconda3/envs/tensorrt/lib/python3.7/site-packages/numpy/testing/_private/utils.py", line 1501, in assert_allclose
    verbose=verbose, header=header, equal_nan=equal_nan)
  File "/home/XXXXXXXX/miniconda3/envs/tensorrt/lib/python3.7/site-packages/numpy/testing/_private/utils.py", line 827, in assert_array_compare
    raise AssertionError(msg)
AssertionError: 
Not equal to tolerance rtol=1e-07, atol=0

Mismatch: 100%
Max absolute difference: 0.5
Max relative difference: 0.2
 x: array([[[3]]], dtype=uint8)
 y: array([[[2.5]]], dtype=float32)