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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 threshold(dataset, value=None, scalars=None, invert=False, continuous=False, preference='cell'):
 """ This filter will apply a ``vtkThreshold`` filter to the input dataset and return the resulting object. This extracts cells where scalar value in each cell satisfies threshold criterion. If scalars is None, the inputs active_scalar is used. Parameters value : float or iterable, optional Single value or (min, max) to be used for the data threshold. If iterable, then length must be 2. If no value is specified, the non-NaN data range will be used to remove any NaN values. scalars : str, optional Name of scalars to threshold on. Defaults to currently active scalars. invert : bool, optional If value is a single value, when invert is True cells are kept when their values are below parameter "value". When invert is False cells are kept when their value is above the threshold "value". Default is False: yielding above the threshold "value". continuous : bool, optional When True, the continuous interval [minimum cell scalar, maxmimum cell scalar] will be used to intersect the threshold bound, rather than the set of discrete scalar values from the vertices. preference : str, optional When scalars is specified, this is the perfered scalar type to search for in the dataset. Must be either ``'point'`` or ``'cell'`` """

        # set the scalaras to threshold on
        if scalars is None:
            field, scalars = dataset.active_scalar_info
        arr, field = get_scalar(dataset, scalars, preference=preference, info=True)

        if arr is None:
            raise AssertionError('No arrays present to threshold.')

        # If using an inverted range, merge the result of two fitlers:
        if isinstance(value, collections.Iterable) and invert:
            valid_range = [np.nanmin(arr), np.nanmax(arr)]
            # Create two thresholds
            t1 = dataset.threshold([valid_range[0], value[0]], scalars=scalars,
                    continuous=continuous, preference=preference, invert=False)
            t2 = dataset.threshold([value[1], valid_range[1]], scalars=scalars,
                    continuous=continuous, preference=preference, invert=False)
            # Use an AppendFilter to merge the two results
            appender = vtk.vtkAppendFilter()
            appender.AddInputData(t1)
            appender.AddInputData(t2)
            appender.Update()
            return _get_output(appender)

        # Run a standard threshold algorithm
        alg = vtk.vtkThreshold()
        alg.SetInputDataObject(dataset)
        alg.SetInputArrayToProcess(0, 0, 0, field, scalars) # args: (idx, port, connection, field, name)
        # set thresholding parameters
        alg.SetUseContinuousCellRange(continuous)
        # use valid range if no value given
        if value is None:
            value = dataset.get_data_range(scalars)
        # check if value is iterable (if so threshold by min max range like ParaView)
        if isinstance(value, collections.Iterable):
            if len(value) != 2:
                raise AssertionError('Value range must be length one for a float value or two for min/max; not ({}).'.format(value))
            alg.ThresholdBetween(value[0], value[1])
        else:
            # just a single value
            if invert:
                alg.ThresholdByLower(value)
            else:
                alg.ThresholdByUpper(value)
        # Run the threshold
        alg.Update()
        return _get_output(alg)