docstr: test forward pass 
docstr_tok: ["test","forward","pass"]
code: def test_train_step(self):
      """test forward pass"""
      (
          chars_seq,
          chars_seq_lengths,
          mel_spec,
          mel_postnet_spec,
          mel_lengths,
          stop_targets,
          speaker_ids,
      ) = self.generate_dummy_inputs()
  
      for idx in mel_lengths:
          stop_targets[:, int(idx.item()) :, 0] = 1.0
  
      stop_targets = stop_targets.view(
          chars_seq.shape[0], stop_targets.size(1) // c.r, -1
      )
      stop_targets = (
          (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
      )
  
      model = Tacotron2(num_chars=24, r=c.r, num_speakers=5)
      # training pass
      output = model(
          chars_seq, chars_seq_lengths, mel_spec, training=True
      )
  
      # check model output shapes
      assert np.all(output[0].shape == mel_spec.shape)
      assert np.all(output[1].shape == mel_spec.shape)
      assert output[2].shape[2] == chars_seq.shape[1]
      assert output[2].shape[1] == (
          mel_spec.shape[1] // model.decoder.r
      )
      assert output[3].shape[1] == (
          mel_spec.shape[1] // model.decoder.r
      )
  
      # inference pass
      output = model(chars_seq, training=False)
  
file_path: TTS/tests/test_tacotron2_tf_model.py
imports: import os
  import unittest
  import numpy
  import tensorflow
  import torch
  from tests import get_tests_input_path
  from TTS.tts.tf.models.tacotron2 import Tacotron2
  from TTS.tts.tf.utils.tflite import convert_tacotron2_to_tflite, load_tflite_model
  from TTS.utils.io import load_config
label_desc: Use image node_id 2 for calling the TacotronTFTrainTest obj's underlying member method code with example usage: obj.test_train_step() without return types