def METHOD_NAME(self, pre_behavioral_model):
    """
    Takes the input from the letters and converts them
    to whatever is needed by the model
    The pre_behavioral_model is a 1D array of letters in an array
    """
    # TODO: set the max_length in the function call
    # Length of behavioral model with which we trained our module
    max_length = 500
    # Convert each of the stratosphere letters to an integer. There are 50
    vocabulary = list('abcdefghiABCDEFGHIrstuvwxyzRSTUVWXYZ1234567890,.+*')
    int_of_letters = {}
    for i, letter in enumerate(vocabulary):
        int_of_letters[letter] = float(i)
    # String to test
    # pre_behavioral_model = "88*y*y*h*h*h*h*h*h*h*y*y*h*h*h*y*y*"
    # Be sure only max_length chars come. Not sure why we receive more
    pre_behavioral_model = pre_behavioral_model[:max_length]
    # Add padding to the letters passed
    # self.print(f'Seq sent: {pre_behavioral_model}')
    pre_behavioral_model += '0' * (max_length - len(pre_behavioral_model))
    # self.print(f'Padded Seq sent: {pre_behavioral_model}')
    # Convert to ndarray
    pre_behavioral_model = np.array(
        [[int_of_letters[i]] for i in pre_behavioral_model]
    )
    # self.print(f'The sequence has shape {pre_behavioral_model.shape}')
    # Reshape into (1, 500, 1) We need the first 1, because this is one sample only, but keras expects a 3d vector
    pre_behavioral_model = np.reshape(
        pre_behavioral_model, (1, max_length, 1)
    )
    # self.print(f'Post Padded Seq sent: {pre_behavioral_model}. Shape: {pre_behavioral_model.shape}')
    return pre_behavioral_model