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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def match_template(template, image, options=None):
 """ Multi channel template matching using simple correlation distance :param template: Template image :param image: Search image :param options: Other options: - distance: Distance measure to use. Default: 'correlation' - normalize: Heatmap values will be in the range of 0 to 1. Default: True - retain_size: Whether to retain the same size as input image. Default: True :return: Heatmap """

    # If the input has max of 3 channels, use the faster OpenCV matching
    if len(image.shape) <= 3 and image.shape[2] <= 3:
        return match_template_opencv(template, image, options)

    op = _DEF_TM_OPT.copy()
    if options is not None:
        op.update(options)

    template = img_utils.gray3(template)
    image = img_utils.gray3(image)

    h, w, d = template.shape
    im_h, im_w = image.shape[:2]

    template_v = template.flatten()

    heatmap = np.zeros((im_h - h, im_w - w))
    for col in range(0, im_w - w):
        for row in range(0, im_h - h):
            cropped_im = image[row:row + h, col:col + w, :]
            cropped_v = cropped_im.flatten()

            if op['distance'] == 'euclidean':
                heatmap[row, col] = scipy.spatial.distance.euclidean(template_v, cropped_v)
            elif op['distance'] == 'correlation':
                heatmap[row, col] = scipy.spatial.distance.correlation(template_v, cropped_v)

    # normalize
    if op['normalize']:
        heatmap /= heatmap.max()

    # size
    if op['retain_size']:
        hmap = np.ones(image.shape[:2]) * heatmap.max()
        h, w = heatmap.shape
        hmap[:h, :w] = heatmap
        heatmap = hmap

    return heatmap