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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 deprocess_input(input_array, input_range=(0, 255)):
 """Utility function to scale the `input_array` to `input_range` throwing away high frequency artifacts. Args: input_array: An N-dim numpy array. input_range: Specifies the input range as a `(min, max)` tuple to rescale the `input_array`. Returns: The rescaled `input_array`. """

    # normalize tensor: center on 0., ensure std is 0.1
    input_array = input_array.copy()
    input_array -= input_array.mean()
    input_array /= (input_array.std() + K.epsilon())
    input_array *= 0.1

    # clip to [0, 1]
    input_array += 0.5
    input_array = np.clip(input_array, 0, 1)

    # Convert to `input_range`
    return (input_range[1] - input_range[0]) * input_array + input_range[0]