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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.