<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def btemp_threshold(img, min_in, max_in, threshold, threshold_out=None, **kwargs):
 """Scale data linearly in two separate regions. This enhancement scales the input data linearly by splitting the data into two regions; min_in to threshold and threshold to max_in. These regions are mapped to 1 to threshold_out and threshold_out to 0 respectively, resulting in the data being "flipped" around the threshold. A default threshold_out is set to `176.0 / 255.0` to match the behavior of the US National Weather Service's forecasting tool called AWIPS. Args: img (XRImage):
 Image object to be scaled min_in (float):
 Minimum input value to scale max_in (float):
 Maximum input value to scale threshold (float):
 Input value where to split data in to two regions threshold_out (float):
 Output value to map the input `threshold` to. Optional, defaults to 176.0 / 255.0. """

    threshold_out = threshold_out if threshold_out is not None else (176 / 255.0)
    low_factor = (threshold_out - 1.) / (min_in - threshold)
    low_offset = 1. + (low_factor * min_in)
    high_factor = threshold_out / (max_in - threshold)
    high_offset = high_factor * max_in

    def _bt_threshold(band_data):
        # expects dask array to be passed
        return da.where(band_data >= threshold,
                        high_offset - high_factor * band_data,
                        low_offset - low_factor * band_data)

    return apply_enhancement(img.data, _bt_threshold, pass_dask=True)