Can someone please explain what this sample function is upto? So there is a function in Dino_Name_Generator at Deeplearning.ai notebook

def sample(parameters, char_to_ix, seed):  
    # Retrieve parameters and relevant shapes from "parameters" dictionary
    Waa, Wax, Wya, by, b = parameters['Waa'], parameters['Wax'],parameters['Wya'], parameters['by'], parameters['b']
    vocab_size = by.shape[0]
    n_a = Waa.shape[1]

    ### START CODE HERE ###
    # Step 1: Create the one-hot vector x for the first character (initializing the sequence generation). (≈1 line)
    x = np.zeros((vocab_size, 1))

    # Step 1': Initialize a_prev as zeros (≈1 line)
    a_prev = np.zeros((n_a, 1))

    # Create an empty list of indices, this is the list which will contain the list of indices of the characters to generate (≈1 line)
    indices = []

    # Idx is a flag to detect a newline character, we initialize it to -1
    idx = -1 

    # Loop over time-steps t. At each time-step, sample a character from a probability distribution and append 
    # its index to "indices". We'll stop if we reach 50 characters (which should be very unlikely with a well 
    # trained model), which helps debugging and prevents entering an infinite loop. 
    counter = 0
    newline_character = char_to_ix['\n']

    while (idx != newline_character and counter != 50):

        # Step 2: Forward propagate x using the equations (1), (2) and (3)
        a = np.tanh(np.dot(Wax, x) + np.dot(Waa, a_prev) + b)
        z = np.dot(Wya, a) + by
        y = softmax(z)

        # for grading purposes
        np.random.seed(counter+seed) 

        # Step 3: Sample the index of a character within the vocabulary from the probability distribution y
        idx = np.random.choice(vocab_size, size=None, p = y.ravel())

        # Append the index to "indices"
        indices.append(idx)

        # Step 4: Overwrite the input character as the one corresponding to the sampled index.
        x = np.zeros((vocab_size, 1))
        x[[idx]] = 1

        # Update "a_prev" to be "a"
        a_prev = a

        # for grading purposes
        seed += 1
        counter +=1


    ### END CODE HERE ###

    if (counter == 50):
        indices.append(char_to_ix['\n'])

    return indices


Can someone please help and explain what benefit of returned indices over normal char_to_integer indices? 

I want to understand the text processing in the link carried out before feeding into the network.
From the link you provided:


  Sample a sequence of characters according to a sequence of probability
  distributions output of the RNN
  
  Arguments:
      parameters -- python dictionary containing the parameters Waa, Wax, Wya, by, and b. 
      char_to_ix -- python dictionary mapping each character to an index.
  
  Returns:
      indices -- a list of length n containing the indices of the sampled characters


You are returning the indices from a dictionary you gave as argument. Why should you use char_to_integer indices.