def deprocess_input(input_array, input_range=(<NUM_LIT:0>, <NUM_LIT:255>)):
<EOL>input_array = input_array.copy()<EOL>input_array -= input_array.mean()<EOL>input_array /= (input_array.std() + K.epsilon())<EOL>input_array *= <NUM_LIT:0.1><EOL>input_array += <NUM_LIT:0.5><EOL>input_array = np.clip(input_array, <NUM_LIT:0>, <NUM_LIT:1>)<EOL>return (input_range[<NUM_LIT:1>] - input_range[<NUM_LIT:0>]) * input_array + input_range[<NUM_LIT:0>]<EOL>
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`.