code: def generate_random_sentences(
      num_sentences=10, max_len=10, vocab_size=10
  ):
      # Generate some random text
      vocab = build_vocab(vocab_size)
  
      text = np.array(
          [
              _generate_sentence(vocab_size, max_len)
              for _ in range(num_sentences)
          ]
      )
  
      return text, vocab
  
imports: import random
  import numpy
  import pytest
  import torch
  from ludwig.constants import ENCODER_OUTPUT
  from ludwig.data.dataset_synthesizer import build_vocab
  from ludwig.encoders.base import Encoder
  from ludwig.encoders.image.base import MLPMixerEncoder, Stacked2DCNN
  from ludwig.encoders.sequence_encoders import ParallelCNN, SequenceEmbedEncoder, StackedCNN, StackedCNNRNN, StackedParallelCNN, StackedRNN
  from ludwig.utils.torch_utils import get_torch_device
  from tests.integration_tests.parameter_update_utils import check_module_parameters_updated
label_desc: Use image node_id 5 for calling a global function with example usage: generate_random_sentences(num_sentences, max_len, vocab_size) and returns: text,  vocab