<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def population_counts( self, population_size, weighted=True, include_missing=False, include_transforms_for_dims=None, prune=False, ):
 """Return counts scaled in proportion to overall population. The return value is a numpy.ndarray object. Count values are scaled proportionally to approximate their value if the entire population had been sampled. This calculation is based on the estimated size of the population provided as *population size*. The remaining arguments have the same meaning as they do for the `.proportions()` method. Example:: np.array([ [5, 2], [5, 3], ]) np.array([ [3000, 1200], [3000, 1800], ]) """

        population_counts = [
            slice_.population_counts(
                population_size,
                weighted=weighted,
                include_missing=include_missing,
                include_transforms_for_dims=include_transforms_for_dims,
                prune=prune,
            )
            for slice_ in self.slices
        ]

        if len(population_counts) > 1:
            return np.array(population_counts)
        return population_counts[0]