why we need to limit tokens to input sequence?

[laptopmutia]

sorry for my ignorance but why we need to limit tokens to input sequence?
I don't understand the function of it, since we training the model why not just put as many token as we can?

# Initialize the function to create the vocabulary 
tokenizer = Tokenizer(filters='', split=" ", lower=False)
# Create the vocabulary 
tokenizer.fit_on_texts([load_doc('resources/bootstrap.vocab')])

# Add one spot for the empty word in the vocabulary 
vocab_size = len(tokenizer.word_index) + 1
# Map the input sentences into the vocabulary indexes
train_sequences = tokenizer.texts_to_sequences(texts)
# The longest set of boostrap tokens
max_sequence = max(len(s) for s in train_sequences)
# Specify how many tokens to have in each input sentence
max_length = 48

def preprocess_data(sequences, features):
    X, y, image_data = list(), list(), list()
    for img_no, seq in enumerate(sequences):
        for i in range(1, len(seq)):
            # Add the sentence until the current count(i) and add the current count to the output
            in_seq, out_seq = seq[:i], seq[i]
            # Pad all the input token sentences to max_sequence
            in_seq = pad_sequences([in_seq], maxlen=max_sequence)[0]
            # Turn the output into one-hot encoding
            out_seq = to_categorical([out_seq], num_classes=vocab_size)[0]
            # Add the corresponding image to the boostrap token file
            image_data.append(features[img_no])
            # Cap the input sentence to 48 tokens and add it
            X.append(in_seq[-48:])
            y.append(out_seq)
    return np.array(X), np.array(y), np.array(image_data)

X, y, image_data = preprocess_data(train_sequences, train_features)

[Emil Wallner]

From my experience the model performed worse when I increased the max_length, but there is no fixed rule. Due to limited compute, most take hyperparameters from other papers.