Fix for pylint errors.

Change-Id: Idd96d7a41fd459c86ab0f6fbb63e5d543509145d

tensorflow/lite/python/convert.py

@@ -103,7 +103,8 @@ class OpsSet(enum.Enum):
   # significantly, but only slightly increases the model size.   
   # WARNING: These ops are currently experimental and have not yet been finalized.
   # They are only compatible with CPU execution, and have not been optimized for production.
-  EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8 = "EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8"
+  EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8 = \
+    "EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8"
 
   def __str__(self):
     return self.value

tensorflow/lite/python/lite.py

@@ -251,7 +251,8 @@ class QuantizationMode(object):
                 self.post_training_fp16())
 
   def activations_type(self):
-    return constants.INT16 if self._is_int16x8_target_required() else constants.INT8
+    return constants.INT16 if self._is_int16x8_target_required() \
+      else constants.INT8
 
   def converter_flags(self, inference_ty=None, inference_input_ty=None):
     """Flags to the converter."""
@@ -262,7 +263,8 @@ class QuantizationMode(object):
 
     if self.training_time_int8_allow_float():
       return {
-          "inference_type": inference_ty if inference_ty else self.activations_type(),
+          "inference_type": inference_ty if inference_ty else \
+            self.activations_type(),
           "inference_input_type":
               inference_input_ty if inference_input_ty else constants.FLOAT,
           "post_training_quantize": False,  # disable dynamic range quantization
@@ -441,7 +443,8 @@ class TFLiteConverterBase(object):
     return _get_grappler_config(optimizers)
 
   def _calibrate_quantize_model(self, result, inference_input_type,
-                                inference_output_type, activations_type, allow_float):
+                                inference_output_type, activations_type,
+                                allow_float):
     """Calibrate and quantize the model."""
     if not isinstance(self.representative_dataset, RepresentativeDataset):
       self.representative_dataset = RepresentativeDataset(

tensorflow/lite/python/lite_test.py

@@ -882,11 +882,15 @@ class FromSessionTest(TestModels, parameterized.TestCase):
 
   @parameterized.named_parameters(
       # Quantize model to Int8: with enable mlir
-      ('UseTfliteBuiltinsIntEnableMLIR', [lite.OpsSet.TFLITE_BUILTINS_INT8], True),
+      ('UseTfliteBuiltinsIntEnableMLIR',
+        [lite.OpsSet.TFLITE_BUILTINS_INT8], True),
       # Quantize model to Int8: with disable mlir
-      ('UseTfliteBuiltinsIntDisableMLIR', [lite.OpsSet.TFLITE_BUILTINS_INT8], False),
+      ('UseTfliteBuiltinsIntDisableMLIR',
+        [lite.OpsSet.TFLITE_BUILTINS_INT8], False),
       # Quantize model to Int16: with disable mlir
-      ('UseTfliteBuiltinsInt16DisableMLIR', [lite.OpsSet.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8], False))
+      ('UseTfliteBuiltinsInt16DisableMLIR',
+        [lite.OpsSet.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8],
+        False))
   def testCalibrateAndQuantizeBuiltinInt(self, supported_ops, enable_mlir):
     with ops.Graph().as_default():
       inp, output, calibration_gen = self._getCalibrationQuantizeModel()

tensorflow/lite/python/optimize/calibrator.py

@@ -78,7 +78,8 @@ class Calibrator(object):
                    computation, useful when targeting an integer-only backend.
                    If False, an error will be thrown if an operation cannot be
                    quantized, otherwise the model will fallback to float ops.
-      activations_type: A tf.dtype representing the desired type for activations.
+      activations_type: A tf.dtype representing the desired type for
+                   activations.
       resize_input: A boolean. True if the shape of the sample data is different
         from the input.
     """

tensorflow/lite/python/optimize/calibrator_test.py

@@ -96,7 +96,8 @@ class CalibratorTest(test_util.TensorFlowTestCase, parameterized.TestCase):
       ('UseActivationTypeInt8 - EnableMlirQuantizer', constants.INT8),
       # Activation type Int16 
       ('UseActivationTypeInt16 - DisableEnableMlirQuantizer', constants.INT16))
-  def test_calibration_with_quantization_multiple_inputs(self, activations_type):
+  def test_calibration_with_quantization_multiple_inputs(self,
+                                                         activations_type):
     # Load multi add model from test data.
     # This model has 4 inputs of size (1, 8, 8, 3).
     model_path = resource_loader.get_path_to_datafile(