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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 get_threshold(threshold_method, threshold_modifier, image, mask=None, labels = None, threshold_range_min = None, threshold_range_max = None, threshold_correction_factor = 1.0, adaptive_window_size = 10, **kwargs):
 """Compute a threshold for an image threshold_method - one of the TM_ methods above threshold_modifier - TM_GLOBAL to calculate one threshold over entire image TM_ADAPTIVE to calculate a per-pixel threshold TM_PER_OBJECT to calculate a different threshold for each object image - a NxM numpy array of the image data Returns a tuple of local_threshold and global_threshold where: * global_threshold is the single number calculated using the threshold method over the whole image * local_threshold is the global_threshold for global methods. For adaptive and per-object thresholding, local_threshold is a matrix of threshold values representing the threshold to be applied at each pixel of the image. Different methods have optional and required parameters: Required: TM_PER_OBJECT: labels - a labels matrix that defines the extents of the individual objects to be thresholded separately. Optional: All: mask - a mask of the significant pixels in the image threshold_range_min, threshold_range_max - constrain the threshold values to be examined to values between these limits threshold_correction_factor - the calculated threshold is multiplied by this number to get the final threshold TM_MOG (mixture of Gaussians):
 object_fraction - fraction of image expected to be occupied by objects (pixels that are above the threshold) TM_OTSU - We have algorithms derived from Otsu. There is a three-class version of Otsu in addition to the two class. There is also an entropy measure in addition to the weighted variance. two_class_otsu - assume that the distribution represents two intensity classes if true, three if false. use_weighted_variance - use Otsu's weighted variance if true, an entropy measure if false assign_middle_to_foreground - assign pixels in the middle class in a three-class Otsu to the foreground if true or the background if false. """

    global_threshold = get_global_threshold(
        threshold_method, image, mask, **kwargs)
    global_threshold *= threshold_correction_factor
    if not threshold_range_min is None:
        global_threshold = max(global_threshold, threshold_range_min)
    if not threshold_range_max is None:
        global_threshold = min(global_threshold, threshold_range_max)
    if threshold_modifier == TM_GLOBAL:
        local_threshold=global_threshold
    elif threshold_modifier == TM_ADAPTIVE:
        local_threshold = get_adaptive_threshold(
            threshold_method, image, global_threshold,
            mask, adaptive_window_size = adaptive_window_size, **kwargs)
        local_threshold = local_threshold * threshold_correction_factor
    elif threshold_modifier == TM_PER_OBJECT:
        local_threshold = get_per_object_threshold(
            threshold_method, image, global_threshold, mask, labels,
            threshold_range_min, threshold_range_max,**kwargs)
        local_threshold = local_threshold * threshold_correction_factor
    else:
        raise NotImplementedError("%s thresholding is not implemented"%(threshold_modifier))
    if isinstance(local_threshold, np.ndarray):
        #
        # Constrain thresholds to within .7 to 1.5 of the global threshold.
        #
        threshold_range_min = max(threshold_range_min, global_threshold * .7)
        threshold_range_max = min(threshold_range_max, global_threshold * 1.5)
        if not threshold_range_min is None:
            local_threshold[local_threshold < threshold_range_min] = \
                threshold_range_min
        if not threshold_range_max is None:
            local_threshold[local_threshold > threshold_range_max] = \
            threshold_range_max
        if (threshold_modifier == TM_PER_OBJECT) and (labels is not None):
            local_threshold[labels == 0] = 1.0
    else:
        if not threshold_range_min is None:
            local_threshold = max(local_threshold, threshold_range_min)
        if not threshold_range_max is None:
            local_threshold = min(local_threshold, threshold_range_max)
    return local_threshold, global_threshold