linter fix

tf/tests/test_tacotron2_tf_model.py

@@ -8,7 +8,6 @@ tf.get_logger().setLevel('INFO')
 from TTS.utils.io import load_config
 from TTS.tf.models.tacotron2 import Tacotron2
 from TTS.tf.utils.tflite import convert_tacotron2_to_tflite, load_tflite_model
-from TTS.utils.synthesis import run_model_tflite, text_to_seqvec
 
 #pylint: disable=unused-variable
 
@@ -16,7 +15,7 @@ torch.manual_seed(1)
 use_cuda = torch.cuda.is_available()
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
-file_path = os.path.dirname(os.path.realpath(__file__)).replace('/tf/','/')
+file_path = os.path.dirname(os.path.realpath(__file__)).replace('/tf/', '/')
 c = load_config(os.path.join(file_path, 'test_config.json'))
 
 
@@ -90,7 +89,7 @@ class TacotronTFTrainTest(unittest.TestCase):
         # inference pass
         output = model(chars_seq, training=False)
 
-    def test_tflite_conversion(self, ):
+    def test_tflite_conversion(self, ):  #pylint:disable=no-self-use
         model = Tacotron2(num_chars=24,
                           num_speakers=0,
                           r=3,
@@ -114,13 +113,13 @@ class TacotronTFTrainTest(unittest.TestCase):
         # init tflite model
         tflite_model = load_tflite_model('test_tacotron2.tflite')
         # fake input
-        inputs = tf.random.uniform([1, 4], maxval=10, dtype=tf.int32)
+        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)
+        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']