code: def test_tflite_conversion(
      self,
  ):  # pylint:disable=no-self-use
      model = Tacotron2(
          num_chars=24,
          num_speakers=0,
          r=3,
          postnet_output_dim=80,
          decoder_output_dim=80,
          attn_type="original",
          attn_win=False,
          attn_norm="sigmoid",
          prenet_type="original",
          prenet_dropout=True,
          forward_attn=False,
          trans_agent=False,
          forward_attn_mask=False,
          location_attn=True,
          attn_K=0,
          separate_stopnet=True,
          bidirectional_decoder=False,
          enable_tflite=True,
      )
      model.build_inference()
      convert_tacotron2_to_tflite(
          model,
          output_path="test_tacotron2.tflite",
          experimental_converter=True,
      )
      # init tflite model
      tflite_model = load_tflite_model("test_tacotron2.tflite")
      # fake input
      inputs = tf.random.uniform(
          [1, 4], maxval=10, dtype=tf.int32
      )  # pylint:disable=unexpected-keyword-arg
      # run inference
      # get input and output details
      input_details = tflite_model.get_input_details()
      output_details = tflite_model.get_output_details()
      # reshape input tensor for the new input shape
      tflite_model.resize_tensor_input(
          input_details[0]["index"], inputs.shape
      )  # pylint:disable=unexpected-keyword-arg
      tflite_model.allocate_tensors()
      detail = input_details[0]
      input_shape = detail["shape"]
      tflite_model.set_tensor(detail["index"], inputs)
      # run the tflite_model
      tflite_model.invoke()
      # collect outputs
      decoder_output = tflite_model.get_tensor(
          output_details[0]["index"]
      )
      postnet_output = tflite_model.get_tensor(
          output_details[1]["index"]
      )
      # remove tflite binary
      os.remove("test_tacotron2.tflite")
  
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 4 for calling the TacotronTFTrainTest obj's underlying member method code with example usage: obj.test_tflite_conversion() without return types