content: -- Task ID: HumanEval/4
  -- Assigned To: Author A
  
  -- Python Implementation:
  
  -- from typing import List
  -- 
  -- 
  -- def mean_absolute_deviation(numbers: List[float]) -> float:
  --     """ For a given list of input numbers, calculate Mean Absolute Deviation
  --     around the mean of this dataset.
  --     Mean Absolute Deviation is the average absolute difference between each
  --     element and a centerpoint (mean in this case):
  --     MAD = average | x - x_mean |
  --     >>> mean_absolute_deviation([1.0, 2.0, 3.0, 4.0])
  --     1.0
  --     """
  --     mean = sum(numbers) / len(numbers)
  --     return sum(abs(x - mean) for x in numbers) / len(numbers)
  -- 
  
  
  -- Haskell Implementation:
  
  -- For a given list of input numbers, calculate Mean Absolute Deviation
  -- around the mean of this dataset.
  -- Mean Absolute Deviation is the average absolute difference between each
  -- element and a centerpoint (mean in this case):
  -- MAD = average | x - x_mean |
  -- >>> mean_absolute_deviation [1.0, 2.0, 3.0, 4.0]
  -- 1.0
  mean_absolute_deviation :: [Float] -> Float
  mean_absolute_deviation numbers = sum ⭐ (map abs (map (\x -> ⭐ x - mean) numbers)) / ⭐ fromIntegral (length numbers)
      where
          mean = ⭐ sum numbers / ⭐ fromIntegral (length numbers)
  