How to use the cntk.io.MinibatchSource function in cntk

def create_reader(path, is_training, input_dim, label_dim):
    return C.io.MinibatchSource(C.io.CTFDeserializer(path, C.io.StreamDefs(
        features = C.io.StreamDef(field='x', shape=input_dim,   is_sparse=True),
        labels   = C.io.StreamDef(field='y', shape=label_dim,   is_sparse=False)
    )), randomize=is_training, max_sweeps = C.io.INFINITELY_REPEAT if is_training else 1)

scale_mode='fill'),
        ]
    else:
        # TODO resize bounding boxes
        transforms += [
            xforms.scale(width=img_width, height=img_height, channels=img_channels, interpolations='linear',
                         scale_mode='pad', pad_value=114),
        ]

    image_source = ImageDeserializer(image_file, StreamDefs(features=StreamDef(field='image', transforms=transforms)))


    # read rois and labels
    roi_source = CTFDeserializer(roi_file, StreamDefs(label=StreamDef(field='rois', shape=output_size)))

    rc = MinibatchSource([image_source, roi_source], randomize=False, trace_level=TraceLevel.Error,
                         multithreaded_deserializer=multithreaded_deserializer, max_samples=max_samples)#, max_epochs=max_epochs)
    return rc

# read images
    nrImages = len(readTable(map_file))
    transforms = [scale(width=img_width, height=img_height, channels=3,
                        scale_mode="pad", pad_value=114, interpolations='linear')]
    image_source = ImageDeserializer(map_file, StreamDefs(features = StreamDef(field='image', transforms=transforms)))

    # read rois and labels
    rois_dim  = 4 * n_rois
    label_dim = n_classes * n_rois
    roi_source = CTFDeserializer(roi_file, StreamDefs(
        rois = StreamDef(field='rois', shape=rois_dim, is_sparse=False)))
    label_source = CTFDeserializer(label_file, StreamDefs(
        roiLabels = StreamDef(field='roiLabels', shape=label_dim, is_sparse=False)))

    # define a composite reader
    mb = MinibatchSource([image_source, roi_source, label_source], max_samples=sys.maxsize, randomize=randomize)
    return (mb, nrImages)

def create_reader(path, is_training, input_dim, label_dim):
    return C.io.MinibatchSource(C.io.CTFDeserializer(path, C.io.StreamDefs(
        features=C.io.StreamDef(field='features', shape=input_dim),
        labels=C.io.StreamDef(field='labels',   shape=label_dim)
    )), randomize=is_training, max_sweeps=C.io.INFINITELY_REPEAT if is_training else 1)

def create_reader(path, is_training, input_dim, label_dim):
    return MinibatchSource(CTFDeserializer(path, StreamDefs(
        features  = StreamDef(field='features', shape=input_dim, is_sparse=False),
        labels    = StreamDef(field='labels',   shape=label_dim, is_sparse=False)
    )), randomize=False, max_sweeps = INFINITELY_REPEAT if is_training else 1)

def create_reader(path, is_training, input_dim, label_dim):
    return cntk.io.MinibatchSource(cntk.io.CTFDeserializer(path, cntk.io.StreamDefs(
        features  = cntk.io.StreamDef(field='features', shape=input_dim),
        labels    = cntk.io.StreamDef(field='labels',   shape=label_dim)
    )), randomize=is_training, max_sweeps = cntk.io.INFINITELY_REPEAT if is_training else 1)

def create_reader(path, is_training, input_dim, label_dim):
    return MinibatchSource(CTFDeserializer(path, StreamDefs(
        features=StreamDef(field='features', shape=input_dim, is_sparse=False),
        labels=StreamDef(field='labels', shape=label_dim, is_sparse=False)
    )), randomize=is_training, epoch_size=INFINITELY_REPEAT if is_training else FULL_DATA_SWEEP)

raise RuntimeError("File '%s' does not exist." %
                           (map_file))

    # transformation pipeline for the features has jitter/crop only when training
    transforms = []
    if train:
        transforms += [
            xforms.crop(crop_type='randomside', side_ratio=0.8, jitter_type='uniratio') # train uses jitter
        ]

    transforms += [
        xforms.scale(width=par_image_width, height=par_image_height, channels=par_num_channels, interpolations='linear')
    ]

    # deserializer
    return cntk.io.MinibatchSource(
        cntk.io.ImageDeserializer(map_file, cntk.io.StreamDefs(
            features = cntk.io.StreamDef(field='image', transforms=transforms), # first column in map file is referred to as 'image'
            labels   = cntk.io.StreamDef(field='label', shape=par_num_classes))),   # and second as 'label'
        randomize=train,
        max_samples=total_number_of_samples,
        multithreaded_deserializer = True)

def create_mb_source(map_file, image_width, image_height, num_channels, num_classes, boTrain):
    transforms = []
    if boTrain:
        # Scale to square-sized image. without this the cropping transform would chop the larger dimension of an
        # image to make it squared, and then take 0.9 crops from within the squared image.
        transforms += [xforms.scale(width=2*image_width, height=2*image_height, channels=num_channels,
                                    interpolations='linear', scale_mode='pad', pad_value=114)]
        transforms += [xforms.crop(crop_type='randomside', side_ratio=0.9, jitter_type='uniratio')]     # Randomly crop square area
    transforms += [xforms.scale(width=image_width, height=image_height, channels=num_channels,          # Scale down and pad
                                interpolations='linear', scale_mode='pad', pad_value=114)]
    if boTrain:
        transforms += [xforms.color(brightness_radius=0.2, contrast_radius=0.2, saturation_radius=0.2)]

    return MinibatchSource(ImageDeserializer(map_file, StreamDefs(
            features  = StreamDef(field='image', transforms=transforms),
            labels    = StreamDef(field='label', shape=num_classes))),
            randomize = boTrain,
            multithreaded_deserializer=True)