diff --git a/videointelligence/samples/analyze/analyze.py b/videointelligence/samples/analyze/analyze.py
index fcfa535c893f..e5e650a8daa9 100644
--- a/videointelligence/samples/analyze/analyze.py
+++ b/videointelligence/samples/analyze/analyze.py
@@ -35,16 +35,17 @@
 import io
 
 from google.cloud import videointelligence
-from google.cloud.videointelligence import enums
 
 
 def analyze_explicit_content(path):
     # [START video_analyze_explicit_content]
     """ Detects explicit content from the GCS path to a video. """
     video_client = videointelligence.VideoIntelligenceServiceClient()
-    features = [videointelligence.enums.Feature.EXPLICIT_CONTENT_DETECTION]
+    features = [videointelligence.Feature.EXPLICIT_CONTENT_DETECTION]
 
-    operation = video_client.annotate_video(input_uri=path, features=features)
+    operation = video_client.annotate_video(
+        request={"features": features, "input_uri": path}
+    )
     print("\nProcessing video for explicit content annotations:")
 
     result = operation.result(timeout=90)
@@ -52,8 +53,8 @@ def analyze_explicit_content(path):
 
     # Retrieve first result because a single video was processed
     for frame in result.annotation_results[0].explicit_annotation.frames:
-        likelihood = enums.Likelihood(frame.pornography_likelihood)
-        frame_time = frame.time_offset.seconds + frame.time_offset.nanos / 1e9
+        likelihood = videointelligence.Likelihood(frame.pornography_likelihood)
+        frame_time = frame.time_offset.seconds + frame.time_offset.microseconds / 1e6
         print("Time: {}s".format(frame_time))
         print("\tpornography: {}".format(likelihood.name))
     # [END video_analyze_explicit_content]
@@ -63,14 +64,14 @@ def analyze_labels(path):
     # [START video_analyze_labels_gcs]
     """ Detects labels given a GCS path. """
     video_client = videointelligence.VideoIntelligenceServiceClient()
-    features = [videointelligence.enums.Feature.LABEL_DETECTION]
+    features = [videointelligence.Feature.LABEL_DETECTION]
 
-    mode = videointelligence.enums.LabelDetectionMode.SHOT_AND_FRAME_MODE
-    config = videointelligence.types.LabelDetectionConfig(label_detection_mode=mode)
-    context = videointelligence.types.VideoContext(label_detection_config=config)
+    mode = videointelligence.LabelDetectionMode.SHOT_AND_FRAME_MODE
+    config = videointelligence.LabelDetectionConfig(label_detection_mode=mode)
+    context = videointelligence.VideoContext(label_detection_config=config)
 
     operation = video_client.annotate_video(
-        input_uri=path, features=features, video_context=context
+        request={"features": features, "input_uri": path, "video_context": context}
     )
     print("\nProcessing video for label annotations:")
 
@@ -89,11 +90,11 @@ def analyze_labels(path):
         for i, segment in enumerate(segment_label.segments):
             start_time = (
                 segment.segment.start_time_offset.seconds
-                + segment.segment.start_time_offset.nanos / 1e9
+                + segment.segment.start_time_offset.microseconds / 1e6
             )
             end_time = (
                 segment.segment.end_time_offset.seconds
-                + segment.segment.end_time_offset.nanos / 1e9
+                + segment.segment.end_time_offset.microseconds / 1e6
             )
             positions = "{}s to {}s".format(start_time, end_time)
             confidence = segment.confidence
@@ -113,11 +114,11 @@ def analyze_labels(path):
         for i, shot in enumerate(shot_label.segments):
             start_time = (
                 shot.segment.start_time_offset.seconds
-                + shot.segment.start_time_offset.nanos / 1e9
+                + shot.segment.start_time_offset.microseconds / 1e6
             )
             end_time = (
                 shot.segment.end_time_offset.seconds
-                + shot.segment.end_time_offset.nanos / 1e9
+                + shot.segment.end_time_offset.microseconds / 1e6
             )
             positions = "{}s to {}s".format(start_time, end_time)
             confidence = shot.confidence
@@ -137,7 +138,7 @@ def analyze_labels(path):
         # Each frame_label_annotation has many frames,
         # here we print information only about the first frame.
         frame = frame_label.frames[0]
-        time_offset = frame.time_offset.seconds + frame.time_offset.nanos / 1e9
+        time_offset = frame.time_offset.seconds + frame.time_offset.microseconds / 1e6
         print("\tFirst frame time offset: {}s".format(time_offset))
         print("\tFirst frame confidence: {}".format(frame.confidence))
         print("\n")
@@ -148,13 +149,13 @@ def analyze_labels_file(path):
     # [START video_analyze_labels]
     """Detect labels given a file path."""
     video_client = videointelligence.VideoIntelligenceServiceClient()
-    features = [videointelligence.enums.Feature.LABEL_DETECTION]
+    features = [videointelligence.Feature.LABEL_DETECTION]
 
     with io.open(path, "rb") as movie:
         input_content = movie.read()
 
     operation = video_client.annotate_video(
-        features=features, input_content=input_content
+        request={"features": features, "input_content": input_content}
     )
     print("\nProcessing video for label annotations:")
 
@@ -173,11 +174,11 @@ def analyze_labels_file(path):
         for i, segment in enumerate(segment_label.segments):
             start_time = (
                 segment.segment.start_time_offset.seconds
-                + segment.segment.start_time_offset.nanos / 1e9
+                + segment.segment.start_time_offset.microseconds / 1e6
             )
             end_time = (
                 segment.segment.end_time_offset.seconds
-                + segment.segment.end_time_offset.nanos / 1e9
+                + segment.segment.end_time_offset.microseconds / 1e6
             )
             positions = "{}s to {}s".format(start_time, end_time)
             confidence = segment.confidence
@@ -197,11 +198,11 @@ def analyze_labels_file(path):
         for i, shot in enumerate(shot_label.segments):
             start_time = (
                 shot.segment.start_time_offset.seconds
-                + shot.segment.start_time_offset.nanos / 1e9
+                + shot.segment.start_time_offset.microseconds / 1e6
             )
             end_time = (
                 shot.segment.end_time_offset.seconds
-                + shot.segment.end_time_offset.nanos / 1e9
+                + shot.segment.end_time_offset.microseconds / 1e6
             )
             positions = "{}s to {}s".format(start_time, end_time)
             confidence = shot.confidence
@@ -221,7 +222,7 @@ def analyze_labels_file(path):
         # Each frame_label_annotation has many frames,
         # here we print information only about the first frame.
         frame = frame_label.frames[0]
-        time_offset = frame.time_offset.seconds + frame.time_offset.nanos / 1e9
+        time_offset = frame.time_offset.seconds + frame.time_offset.microseconds / 1e6
         print("\tFirst frame time offset: {}s".format(time_offset))
         print("\tFirst frame confidence: {}".format(frame.confidence))
         print("\n")
@@ -232,8 +233,10 @@ def analyze_shots(path):
     # [START video_analyze_shots]
     """ Detects camera shot changes. """
     video_client = videointelligence.VideoIntelligenceServiceClient()
-    features = [videointelligence.enums.Feature.SHOT_CHANGE_DETECTION]
-    operation = video_client.annotate_video(input_uri=path, features=features)
+    features = [videointelligence.Feature.SHOT_CHANGE_DETECTION]
+    operation = video_client.annotate_video(
+        request={"features": features, "input_uri": path}
+    )
     print("\nProcessing video for shot change annotations:")
 
     result = operation.result(timeout=90)
@@ -241,8 +244,12 @@ def analyze_shots(path):
 
     # first result is retrieved because a single video was processed
     for i, shot in enumerate(result.annotation_results[0].shot_annotations):
-        start_time = shot.start_time_offset.seconds + shot.start_time_offset.nanos / 1e9
-        end_time = shot.end_time_offset.seconds + shot.end_time_offset.nanos / 1e9
+        start_time = (
+            shot.start_time_offset.seconds + shot.start_time_offset.microseconds / 1e6
+        )
+        end_time = (
+            shot.end_time_offset.seconds + shot.end_time_offset.microseconds / 1e6
+        )
         print("\tShot {}: {} to {}".format(i, start_time, end_time))
     # [END video_analyze_shots]
 
@@ -253,17 +260,19 @@ def speech_transcription(path):
     from google.cloud import videointelligence
 
     video_client = videointelligence.VideoIntelligenceServiceClient()
-    features = [videointelligence.enums.Feature.SPEECH_TRANSCRIPTION]
+    features = [videointelligence.Feature.SPEECH_TRANSCRIPTION]
 
-    config = videointelligence.types.SpeechTranscriptionConfig(
+    config = videointelligence.SpeechTranscriptionConfig(
         language_code="en-US", enable_automatic_punctuation=True
     )
-    video_context = videointelligence.types.VideoContext(
-        speech_transcription_config=config
-    )
+    video_context = videointelligence.VideoContext(speech_transcription_config=config)
 
     operation = video_client.annotate_video(
-        input_uri=path, features=features, video_context=video_context
+        request={
+            "features": features,
+            "input_uri": path,
+            "video_context": video_context,
+        }
     )
 
     print("\nProcessing video for speech transcription.")
@@ -292,8 +301,8 @@ def speech_transcription(path):
                 end_time = word_info.end_time
                 print(
                     "\t{}s - {}s: {}".format(
-                        start_time.seconds + start_time.nanos * 1e-9,
-                        end_time.seconds + end_time.nanos * 1e-9,
+                        start_time.seconds + start_time.microseconds * 1e-6,
+                        end_time.seconds + end_time.microseconds * 1e-6,
                         word,
                     )
                 )
@@ -306,9 +315,11 @@ def video_detect_text_gcs(input_uri):
     from google.cloud import videointelligence
 
     video_client = videointelligence.VideoIntelligenceServiceClient()
-    features = [videointelligence.enums.Feature.TEXT_DETECTION]
+    features = [videointelligence.Feature.TEXT_DETECTION]
 
-    operation = video_client.annotate_video(input_uri=input_uri, features=features)
+    operation = video_client.annotate_video(
+        request={"features": features, "input_uri": input_uri}
+    )
 
     print("\nProcessing video for text detection.")
     result = operation.result(timeout=600)
@@ -325,8 +336,8 @@ def video_detect_text_gcs(input_uri):
         end_time = text_segment.segment.end_time_offset
         print(
             "start_time: {}, end_time: {}".format(
-                start_time.seconds + start_time.nanos * 1e-9,
-                end_time.seconds + end_time.nanos * 1e-9,
+                start_time.seconds + start_time.microseconds * 1e-6,
+                end_time.seconds + end_time.microseconds * 1e-6,
             )
         )
 
@@ -337,7 +348,7 @@ def video_detect_text_gcs(input_uri):
         time_offset = frame.time_offset
         print(
             "Time offset for the first frame: {}".format(
-                time_offset.seconds + time_offset.nanos * 1e-9
+                time_offset.seconds + time_offset.microseconds * 1e-6
             )
         )
         print("Rotated Bounding Box Vertices:")
@@ -352,16 +363,18 @@ def video_detect_text(path):
     from google.cloud import videointelligence
 
     video_client = videointelligence.VideoIntelligenceServiceClient()
-    features = [videointelligence.enums.Feature.TEXT_DETECTION]
-    video_context = videointelligence.types.VideoContext()
+    features = [videointelligence.Feature.TEXT_DETECTION]
+    video_context = videointelligence.VideoContext()
 
     with io.open(path, "rb") as file:
         input_content = file.read()
 
     operation = video_client.annotate_video(
-        input_content=input_content,  # the bytes of the video file
-        features=features,
-        video_context=video_context,
+        request={
+            "features": features,
+            "input_content": input_content,
+            "video_context": video_context,
+        }
     )
 
     print("\nProcessing video for text detection.")
@@ -379,8 +392,8 @@ def video_detect_text(path):
         end_time = text_segment.segment.end_time_offset
         print(
             "start_time: {}, end_time: {}".format(
-                start_time.seconds + start_time.nanos * 1e-9,
-                end_time.seconds + end_time.nanos * 1e-9,
+                start_time.seconds + start_time.microseconds * 1e-6,
+                end_time.seconds + end_time.microseconds * 1e-6,
             )
         )
 
@@ -391,7 +404,7 @@ def video_detect_text(path):
         time_offset = frame.time_offset
         print(
             "Time offset for the first frame: {}".format(
-                time_offset.seconds + time_offset.nanos * 1e-9
+                time_offset.seconds + time_offset.microseconds * 1e-6
             )
         )
         print("Rotated Bounding Box Vertices:")
@@ -406,8 +419,10 @@ def track_objects_gcs(gcs_uri):
     from google.cloud import videointelligence
 
     video_client = videointelligence.VideoIntelligenceServiceClient()
-    features = [videointelligence.enums.Feature.OBJECT_TRACKING]
-    operation = video_client.annotate_video(input_uri=gcs_uri, features=features)
+    features = [videointelligence.Feature.OBJECT_TRACKING]
+    operation = video_client.annotate_video(
+        request={"features": features, "input_uri": gcs_uri}
+    )
     print("\nProcessing video for object annotations.")
 
     result = operation.result(timeout=300)
@@ -424,9 +439,9 @@ def track_objects_gcs(gcs_uri):
         print(
             "Segment: {}s to {}s".format(
                 object_annotation.segment.start_time_offset.seconds
-                + object_annotation.segment.start_time_offset.nanos / 1e9,
+                + object_annotation.segment.start_time_offset.microseconds / 1e6,
                 object_annotation.segment.end_time_offset.seconds
-                + object_annotation.segment.end_time_offset.nanos / 1e9,
+                + object_annotation.segment.end_time_offset.microseconds / 1e6,
             )
         )
 
@@ -437,7 +452,7 @@ def track_objects_gcs(gcs_uri):
         box = frame.normalized_bounding_box
         print(
             "Time offset of the first frame: {}s".format(
-                frame.time_offset.seconds + frame.time_offset.nanos / 1e9
+                frame.time_offset.seconds + frame.time_offset.microseconds / 1e6
             )
         )
         print("Bounding box position:")
@@ -455,13 +470,13 @@ def track_objects(path):
     from google.cloud import videointelligence
 
     video_client = videointelligence.VideoIntelligenceServiceClient()
-    features = [videointelligence.enums.Feature.OBJECT_TRACKING]
+    features = [videointelligence.Feature.OBJECT_TRACKING]
 
     with io.open(path, "rb") as file:
         input_content = file.read()
 
     operation = video_client.annotate_video(
-        input_content=input_content, features=features
+        request={"features": features, "input_content": input_content}
     )
     print("\nProcessing video for object annotations.")
 
@@ -480,9 +495,9 @@ def track_objects(path):
     print(
         "Segment: {}s to {}s".format(
             object_annotation.segment.start_time_offset.seconds
-            + object_annotation.segment.start_time_offset.nanos / 1e9,
+            + object_annotation.segment.start_time_offset.microseconds / 1e6,
             object_annotation.segment.end_time_offset.seconds
-            + object_annotation.segment.end_time_offset.nanos / 1e9,
+            + object_annotation.segment.end_time_offset.microseconds / 1e6,
         )
     )
 
@@ -493,7 +508,7 @@ def track_objects(path):
     box = frame.normalized_bounding_box
     print(
         "Time offset of the first frame: {}s".format(
-            frame.time_offset.seconds + frame.time_offset.nanos / 1e9
+            frame.time_offset.seconds + frame.time_offset.microseconds / 1e6
         )
     )
     print("Bounding box position:")
diff --git a/videointelligence/samples/analyze/analyze_test.py b/videointelligence/samples/analyze/analyze_test.py
index 6623fd326983..2da6cb760f1b 100644
--- a/videointelligence/samples/analyze/analyze_test.py
+++ b/videointelligence/samples/analyze/analyze_test.py
@@ -74,7 +74,7 @@ def test_speech_transcription(capsys):
 def test_detect_text_gcs(capsys):
     analyze.video_detect_text_gcs("gs://cloud-samples-data/video/googlework_tiny.mp4")
     out, _ = capsys.readouterr()
-    assert 'Text' in out
+    assert "Text" in out
 
 
 # Flaky timeout
@@ -82,7 +82,7 @@ def test_detect_text_gcs(capsys):
 def test_detect_text(capsys):
     analyze.video_detect_text("resources/googlework_tiny.mp4")
     out, _ = capsys.readouterr()
-    assert 'Text' in out
+    assert "Text" in out
 
 
 # Flaky timeout
diff --git a/videointelligence/samples/analyze/beta_snippets.py b/videointelligence/samples/analyze/beta_snippets.py
index 33d221ce4b90..0e484f8a6293 100644
--- a/videointelligence/samples/analyze/beta_snippets.py
+++ b/videointelligence/samples/analyze/beta_snippets.py
@@ -52,17 +52,19 @@ def speech_transcription(input_uri):
 
     video_client = videointelligence.VideoIntelligenceServiceClient()
 
-    features = [videointelligence.enums.Feature.SPEECH_TRANSCRIPTION]
+    features = [videointelligence.Feature.SPEECH_TRANSCRIPTION]
 
-    config = videointelligence.types.SpeechTranscriptionConfig(
+    config = videointelligence.SpeechTranscriptionConfig(
         language_code="en-US", enable_automatic_punctuation=True
     )
-    video_context = videointelligence.types.VideoContext(
-        speech_transcription_config=config
-    )
+    video_context = videointelligence.VideoContext(speech_transcription_config=config)
 
     operation = video_client.annotate_video(
-        input_uri=input_uri, features=features, video_context=video_context
+        request={
+            "features": features,
+            "input_uri": input_uri,
+            "video_context": video_context,
+        }
     )
 
     print("\nProcessing video for speech transcription.")
@@ -91,8 +93,8 @@ def speech_transcription(input_uri):
                 end_time = word_info.end_time
                 print(
                     "\t{}s - {}s: {}".format(
-                        start_time.seconds + start_time.nanos * 1e-9,
-                        end_time.seconds + end_time.nanos * 1e-9,
+                        start_time.seconds + start_time.microseconds * 1e-6,
+                        end_time.seconds + end_time.microseconds * 1e-6,
                         word,
                     )
                 )
@@ -105,9 +107,11 @@ def video_detect_text_gcs(input_uri):
     from google.cloud import videointelligence_v1p2beta1 as videointelligence
 
     video_client = videointelligence.VideoIntelligenceServiceClient()
-    features = [videointelligence.enums.Feature.TEXT_DETECTION]
+    features = [videointelligence.Feature.TEXT_DETECTION]
 
-    operation = video_client.annotate_video(input_uri=input_uri, features=features)
+    operation = video_client.annotate_video(
+        request={"features": features, "input_uri": input_uri}
+    )
 
     print("\nProcessing video for text detection.")
     result = operation.result(timeout=300)
@@ -125,8 +129,8 @@ def video_detect_text_gcs(input_uri):
     end_time = text_segment.segment.end_time_offset
     print(
         "start_time: {}, end_time: {}".format(
-            start_time.seconds + start_time.nanos * 1e-9,
-            end_time.seconds + end_time.nanos * 1e-9,
+            start_time.seconds + start_time.microseconds * 1e-6,
+            end_time.seconds + end_time.microseconds * 1e-6,
         )
     )
 
@@ -137,7 +141,7 @@ def video_detect_text_gcs(input_uri):
     time_offset = frame.time_offset
     print(
         "Time offset for the first frame: {}".format(
-            time_offset.seconds + time_offset.nanos * 1e-9
+            time_offset.seconds + time_offset.microseconds * 1e-6
         )
     )
     print("Rotated Bounding Box Vertices:")
@@ -153,16 +157,18 @@ def video_detect_text(path):
     from google.cloud import videointelligence_v1p2beta1 as videointelligence
 
     video_client = videointelligence.VideoIntelligenceServiceClient()
-    features = [videointelligence.enums.Feature.TEXT_DETECTION]
-    video_context = videointelligence.types.VideoContext()
+    features = [videointelligence.Feature.TEXT_DETECTION]
+    video_context = videointelligence.VideoContext()
 
     with io.open(path, "rb") as file:
         input_content = file.read()
 
     operation = video_client.annotate_video(
-        input_content=input_content,  # the bytes of the video file
-        features=features,
-        video_context=video_context,
+        request={
+            "features": features,
+            "input_content": input_content,
+            "video_context": video_context,
+        }
     )
 
     print("\nProcessing video for text detection.")
@@ -181,8 +187,8 @@ def video_detect_text(path):
     end_time = text_segment.segment.end_time_offset
     print(
         "start_time: {}, end_time: {}".format(
-            start_time.seconds + start_time.nanos * 1e-9,
-            end_time.seconds + end_time.nanos * 1e-9,
+            start_time.seconds + start_time.microseconds * 1e-6,
+            end_time.seconds + end_time.microseconds * 1e-6,
         )
     )
 
@@ -193,7 +199,7 @@ def video_detect_text(path):
     time_offset = frame.time_offset
     print(
         "Time offset for the first frame: {}".format(
-            time_offset.seconds + time_offset.nanos * 1e-9
+            time_offset.seconds + time_offset.microseconds * 1e-6
         )
     )
     print("Rotated Bounding Box Vertices:")
@@ -211,9 +217,13 @@ def track_objects_gcs(gcs_uri):
     # It is recommended to use location_id as 'us-east1' for the best latency
     # due to different types of processors used in this region and others.
     video_client = videointelligence.VideoIntelligenceServiceClient()
-    features = [videointelligence.enums.Feature.OBJECT_TRACKING]
+    features = [videointelligence.Feature.OBJECT_TRACKING]
     operation = video_client.annotate_video(
-        input_uri=gcs_uri, features=features, location_id="us-east1"
+        request={
+            "features": features,
+            "input_uri": gcs_uri,
+            "location_id": "us-east1",
+        }
     )
     print("\nProcessing video for object annotations.")
 
@@ -233,9 +243,9 @@ def track_objects_gcs(gcs_uri):
     print(
         "Segment: {}s to {}s".format(
             object_annotation.segment.start_time_offset.seconds
-            + object_annotation.segment.start_time_offset.nanos / 1e9,
+            + object_annotation.segment.start_time_offset.microseconds / 1e6,
             object_annotation.segment.end_time_offset.seconds
-            + object_annotation.segment.end_time_offset.nanos / 1e9,
+            + object_annotation.segment.end_time_offset.microseconds / 1e6,
         )
     )
 
@@ -246,7 +256,7 @@ def track_objects_gcs(gcs_uri):
     box = frame.normalized_bounding_box
     print(
         "Time offset of the first frame: {}s".format(
-            frame.time_offset.seconds + frame.time_offset.nanos / 1e9
+            frame.time_offset.seconds + frame.time_offset.microseconds / 1e6
         )
     )
     print("Bounding box position:")
@@ -265,7 +275,7 @@ def track_objects(path):
     from google.cloud import videointelligence_v1p2beta1 as videointelligence
 
     video_client = videointelligence.VideoIntelligenceServiceClient()
-    features = [videointelligence.enums.Feature.OBJECT_TRACKING]
+    features = [videointelligence.Feature.OBJECT_TRACKING]
 
     with io.open(path, "rb") as file:
         input_content = file.read()
@@ -273,7 +283,11 @@ def track_objects(path):
     # It is recommended to use location_id as 'us-east1' for the best latency
     # due to different types of processors used in this region and others.
     operation = video_client.annotate_video(
-        input_content=input_content, features=features, location_id="us-east1"
+        request={
+            "features": features,
+            "input_content": input_content,
+            "location_id": "us-east1",
+        }
     )
     print("\nProcessing video for object annotations.")
 
@@ -293,9 +307,9 @@ def track_objects(path):
     print(
         "Segment: {}s to {}s".format(
             object_annotation.segment.start_time_offset.seconds
-            + object_annotation.segment.start_time_offset.nanos / 1e9,
+            + object_annotation.segment.start_time_offset.microseconds / 1e6,
             object_annotation.segment.end_time_offset.seconds
-            + object_annotation.segment.end_time_offset.nanos / 1e9,
+            + object_annotation.segment.end_time_offset.microseconds / 1e6,
         )
     )
 
@@ -306,7 +320,7 @@ def track_objects(path):
     box = frame.normalized_bounding_box
     print(
         "Time offset of the first frame: {}s".format(
-            frame.time_offset.seconds + frame.time_offset.nanos / 1e9
+            frame.time_offset.seconds + frame.time_offset.microseconds / 1e6
         )
     )
     print("Bounding box position:")
@@ -328,12 +342,12 @@ def detect_labels_streaming(path):
     client = videointelligence.StreamingVideoIntelligenceServiceClient()
 
     # Set streaming config.
-    config = videointelligence.types.StreamingVideoConfig(
-        feature=(videointelligence.enums.StreamingFeature.STREAMING_LABEL_DETECTION)
+    config = videointelligence.StreamingVideoConfig(
+        feature=(videointelligence.StreamingFeature.STREAMING_LABEL_DETECTION)
     )
 
     # config_request should be the first in the stream of requests.
-    config_request = videointelligence.types.StreamingAnnotateVideoRequest(
+    config_request = videointelligence.StreamingAnnotateVideoRequest(
         video_config=config
     )
 
@@ -352,9 +366,7 @@ def detect_labels_streaming(path):
     def stream_generator():
         yield config_request
         for chunk in stream:
-            yield videointelligence.types.StreamingAnnotateVideoRequest(
-                input_content=chunk
-            )
+            yield videointelligence.StreamingAnnotateVideoRequest(input_content=chunk)
 
     requests = stream_generator()
 
@@ -380,7 +392,9 @@ def stream_generator():
         for annotation in label_annotations:
             # Each annotation has one frame, which has a timeoffset.
             frame = annotation.frames[0]
-            time_offset = frame.time_offset.seconds + frame.time_offset.nanos / 1e9
+            time_offset = (
+                frame.time_offset.seconds + frame.time_offset.microseconds / 1e6
+            )
 
             description = annotation.entity.description
             confidence = annotation.frames[0].confidence
@@ -400,14 +414,12 @@ def detect_shot_change_streaming(path):
     client = videointelligence.StreamingVideoIntelligenceServiceClient()
 
     # Set streaming config.
-    config = videointelligence.types.StreamingVideoConfig(
-        feature=(
-            videointelligence.enums.StreamingFeature.STREAMING_SHOT_CHANGE_DETECTION
-        )
+    config = videointelligence.StreamingVideoConfig(
+        feature=(videointelligence.StreamingFeature.STREAMING_SHOT_CHANGE_DETECTION)
     )
 
     # config_request should be the first in the stream of requests.
-    config_request = videointelligence.types.StreamingAnnotateVideoRequest(
+    config_request = videointelligence.StreamingAnnotateVideoRequest(
         video_config=config
     )
 
@@ -426,9 +438,7 @@ def detect_shot_change_streaming(path):
     def stream_generator():
         yield config_request
         for chunk in stream:
-            yield videointelligence.types.StreamingAnnotateVideoRequest(
-                input_content=chunk
-            )
+            yield videointelligence.StreamingAnnotateVideoRequest(input_content=chunk)
 
     requests = stream_generator()
 
@@ -448,11 +458,11 @@ def stream_generator():
         for annotation in response.annotation_results.shot_annotations:
             start = (
                 annotation.start_time_offset.seconds
-                + annotation.start_time_offset.nanos / 1e9
+                + annotation.start_time_offset.microseconds / 1e6
             )
             end = (
                 annotation.end_time_offset.seconds
-                + annotation.end_time_offset.nanos / 1e9
+                + annotation.end_time_offset.microseconds / 1e6
             )
 
             print("Shot: {}s to {}s".format(start, end))
@@ -468,12 +478,12 @@ def track_objects_streaming(path):
     client = videointelligence.StreamingVideoIntelligenceServiceClient()
 
     # Set streaming config.
-    config = videointelligence.types.StreamingVideoConfig(
-        feature=(videointelligence.enums.StreamingFeature.STREAMING_OBJECT_TRACKING)
+    config = videointelligence.StreamingVideoConfig(
+        feature=(videointelligence.StreamingFeature.STREAMING_OBJECT_TRACKING)
     )
 
     # config_request should be the first in the stream of requests.
-    config_request = videointelligence.types.StreamingAnnotateVideoRequest(
+    config_request = videointelligence.StreamingAnnotateVideoRequest(
         video_config=config
     )
 
@@ -492,9 +502,7 @@ def track_objects_streaming(path):
     def stream_generator():
         yield config_request
         for chunk in stream:
-            yield videointelligence.types.StreamingAnnotateVideoRequest(
-                input_content=chunk
-            )
+            yield videointelligence.StreamingAnnotateVideoRequest(input_content=chunk)
 
     requests = stream_generator()
 
@@ -520,7 +528,9 @@ def stream_generator():
         for annotation in object_annotations:
             # Each annotation has one frame, which has a timeoffset.
             frame = annotation.frames[0]
-            time_offset = frame.time_offset.seconds + frame.time_offset.nanos / 1e9
+            time_offset = (
+                frame.time_offset.seconds + frame.time_offset.microseconds / 1e6
+            )
 
             description = annotation.entity.description
             confidence = annotation.confidence
@@ -557,14 +567,14 @@ def detect_explicit_content_streaming(path):
     client = videointelligence.StreamingVideoIntelligenceServiceClient()
 
     # Set streaming config.
-    config = videointelligence.types.StreamingVideoConfig(
+    config = videointelligence.StreamingVideoConfig(
         feature=(
-            videointelligence.enums.StreamingFeature.STREAMING_EXPLICIT_CONTENT_DETECTION
+            videointelligence.StreamingFeature.STREAMING_EXPLICIT_CONTENT_DETECTION
         )
     )
 
     # config_request should be the first in the stream of requests.
-    config_request = videointelligence.types.StreamingAnnotateVideoRequest(
+    config_request = videointelligence.StreamingAnnotateVideoRequest(
         video_config=config
     )
 
@@ -583,9 +593,7 @@ def detect_explicit_content_streaming(path):
     def stream_generator():
         yield config_request
         for chunk in stream:
-            yield videointelligence.types.StreamingAnnotateVideoRequest(
-                input_content=chunk
-            )
+            yield videointelligence.StreamingAnnotateVideoRequest(input_content=chunk)
 
     requests = stream_generator()
 
@@ -603,8 +611,10 @@ def stream_generator():
             break
 
         for frame in response.annotation_results.explicit_annotation.frames:
-            time_offset = frame.time_offset.seconds + frame.time_offset.nanos / 1e9
-            pornography_likelihood = videointelligence.enums.Likelihood(
+            time_offset = (
+                frame.time_offset.seconds + frame.time_offset.microseconds / 1e6
+            )
+            pornography_likelihood = videointelligence.Likelihood(
                 frame.pornography_likelihood
             )
 
@@ -624,19 +634,19 @@ def annotation_to_storage_streaming(path, output_uri):
 
     # Set streaming config specifying the output_uri.
     # The output_uri is the prefix of the actual output files.
-    storage_config = videointelligence.types.StreamingStorageConfig(
+    storage_config = videointelligence.StreamingStorageConfig(
         enable_storage_annotation_result=True,
         annotation_result_storage_directory=output_uri,
     )
     # Here we use label detection as an example.
     # All features support output to GCS.
-    config = videointelligence.types.StreamingVideoConfig(
-        feature=(videointelligence.enums.StreamingFeature.STREAMING_LABEL_DETECTION),
+    config = videointelligence.StreamingVideoConfig(
+        feature=(videointelligence.StreamingFeature.STREAMING_LABEL_DETECTION),
         storage_config=storage_config,
     )
 
     # config_request should be the first in the stream of requests.
-    config_request = videointelligence.types.StreamingAnnotateVideoRequest(
+    config_request = videointelligence.StreamingAnnotateVideoRequest(
         video_config=config
     )
 
@@ -655,9 +665,7 @@ def annotation_to_storage_streaming(path, output_uri):
     def stream_generator():
         yield config_request
         for chunk in stream:
-            yield videointelligence.types.StreamingAnnotateVideoRequest(
-                input_content=chunk
-            )
+            yield videointelligence.StreamingAnnotateVideoRequest(input_content=chunk)
 
     requests = stream_generator()
 
@@ -682,7 +690,6 @@ def streaming_automl_classification(path, project_id, model_id):
     import io
 
     from google.cloud import videointelligence_v1p3beta1 as videointelligence
-    from google.cloud.videointelligence_v1p3beta1 import enums
 
     # path = 'path_to_file'
     # project_id = 'gcp_project_id'
@@ -695,17 +702,17 @@ def streaming_automl_classification(path, project_id, model_id):
     )
 
     # Here we use classification as an example.
-    automl_config = videointelligence.types.StreamingAutomlClassificationConfig(
+    automl_config = videointelligence.StreamingAutomlClassificationConfig(
         model_name=model_path
     )
 
-    video_config = videointelligence.types.StreamingVideoConfig(
-        feature=enums.StreamingFeature.STREAMING_AUTOML_CLASSIFICATION,
+    video_config = videointelligence.StreamingVideoConfig(
+        feature=videointelligence.StreamingFeature.STREAMING_AUTOML_CLASSIFICATION,
         automl_classification_config=automl_config,
     )
 
     # config_request should be the first in the stream of requests.
-    config_request = videointelligence.types.StreamingAnnotateVideoRequest(
+    config_request = videointelligence.StreamingAnnotateVideoRequest(
         video_config=video_config
     )
 
@@ -727,9 +734,7 @@ def streaming_automl_classification(path, project_id, model_id):
     def stream_generator():
         yield config_request
         for chunk in stream:
-            yield videointelligence.types.StreamingAnnotateVideoRequest(
-                input_content=chunk
-            )
+            yield videointelligence.StreamingAnnotateVideoRequest(input_content=chunk)
 
     requests = stream_generator()
 
diff --git a/videointelligence/samples/analyze/beta_snippets_test.py b/videointelligence/samples/analyze/beta_snippets_test.py
index bcb6c7bc489e..1c9da43acf12 100644
--- a/videointelligence/samples/analyze/beta_snippets_test.py
+++ b/videointelligence/samples/analyze/beta_snippets_test.py
@@ -15,13 +15,13 @@
 # limitations under the License.
 
 import os
+from urllib.request import urlopen
 import uuid
 
 import backoff
 from google.api_core.exceptions import Conflict
 from google.cloud import storage
 import pytest
-from six.moves.urllib.request import urlopen
 
 import beta_snippets
 
@@ -55,7 +55,7 @@ def video_path(tmpdir_factory):
 @pytest.fixture(scope="function")
 def bucket():
     # Create a temporaty bucket to store annotation output.
-    bucket_name = f'tmp-{uuid.uuid4().hex}'
+    bucket_name = f"tmp-{uuid.uuid4().hex}"
     storage_client = storage.Client()
     bucket = storage_client.create_bucket(bucket_name)
 
@@ -128,7 +128,7 @@ def test_detect_text(capsys):
     in_file = "./resources/googlework_tiny.mp4"
     beta_snippets.video_detect_text(in_file)
     out, _ = capsys.readouterr()
-    assert 'Text' in out
+    assert "Text" in out
 
 
 # Flaky timeout
@@ -137,7 +137,7 @@ def test_detect_text_gcs(capsys):
     in_file = "gs://python-docs-samples-tests/video/googlework_tiny.mp4"
     beta_snippets.video_detect_text_gcs(in_file)
     out, _ = capsys.readouterr()
-    assert 'Text' in out
+    assert "Text" in out
 
 
 # Flaky InvalidArgument
diff --git a/videointelligence/samples/analyze/noxfile.py b/videointelligence/samples/analyze/noxfile.py
index b90eef00f2d9..ab2c49227c3b 100644
--- a/videointelligence/samples/analyze/noxfile.py
+++ b/videointelligence/samples/analyze/noxfile.py
@@ -37,28 +37,25 @@
 
 TEST_CONFIG = {
     # You can opt out from the test for specific Python versions.
-    'ignored_versions': ["2.7"],
-
+    "ignored_versions": ["2.7"],
     # Old samples are opted out of enforcing Python type hints
     # All new samples should feature them
-    'enforce_type_hints': False,
-
+    "enforce_type_hints": False,
     # An envvar key for determining the project id to use. Change it
     # to 'BUILD_SPECIFIC_GCLOUD_PROJECT' if you want to opt in using a
     # build specific Cloud project. You can also use your own string
     # to use your own Cloud project.
-    'gcloud_project_env': 'GOOGLE_CLOUD_PROJECT',
+    "gcloud_project_env": "GOOGLE_CLOUD_PROJECT",
     # 'gcloud_project_env': 'BUILD_SPECIFIC_GCLOUD_PROJECT',
-
     # A dictionary you want to inject into your test. Don't put any
     # secrets here. These values will override predefined values.
-    'envs': {},
+    "envs": {},
 }
 
 
 try:
     # Ensure we can import noxfile_config in the project's directory.
-    sys.path.append('.')
+    sys.path.append(".")
     from noxfile_config import TEST_CONFIG_OVERRIDE
 except ImportError as e:
     print("No user noxfile_config found: detail: {}".format(e))
@@ -73,12 +70,12 @@ def get_pytest_env_vars():
     ret = {}
 
     # Override the GCLOUD_PROJECT and the alias.
-    env_key = TEST_CONFIG['gcloud_project_env']
+    env_key = TEST_CONFIG["gcloud_project_env"]
     # This should error out if not set.
-    ret['GOOGLE_CLOUD_PROJECT'] = os.environ[env_key]
+    ret["GOOGLE_CLOUD_PROJECT"] = os.environ[env_key]
 
     # Apply user supplied envs.
-    ret.update(TEST_CONFIG['envs'])
+    ret.update(TEST_CONFIG["envs"])
     return ret
 
 
@@ -87,7 +84,7 @@ def get_pytest_env_vars():
 ALL_VERSIONS = ["2.7", "3.6", "3.7", "3.8"]
 
 # Any default versions that should be ignored.
-IGNORED_VERSIONS = TEST_CONFIG['ignored_versions']
+IGNORED_VERSIONS = TEST_CONFIG["ignored_versions"]
 
 TESTED_VERSIONS = sorted([v for v in ALL_VERSIONS if v not in IGNORED_VERSIONS])
 
@@ -136,7 +133,7 @@ def _determine_local_import_names(start_dir):
 
 @nox.session
 def lint(session):
-    if not TEST_CONFIG['enforce_type_hints']:
+    if not TEST_CONFIG["enforce_type_hints"]:
         session.install("flake8", "flake8-import-order")
     else:
         session.install("flake8", "flake8-import-order", "flake8-annotations")
@@ -145,9 +142,11 @@ def lint(session):
     args = FLAKE8_COMMON_ARGS + [
         "--application-import-names",
         ",".join(local_names),
-        "."
+        ".",
     ]
     session.run("flake8", *args)
+
+
 #
 # Black
 #
@@ -160,6 +159,7 @@ def blacken(session):
 
     session.run("black", *python_files)
 
+
 #
 # Sample Tests
 #
@@ -199,9 +199,9 @@ def py(session):
     if session.python in TESTED_VERSIONS:
         _session_tests(session)
     else:
-        session.skip("SKIPPED: {} tests are disabled for this sample.".format(
-            session.python
-        ))
+        session.skip(
+            "SKIPPED: {} tests are disabled for this sample.".format(session.python)
+        )
 
 
 #
diff --git a/videointelligence/samples/analyze/video_detect_faces_beta.py b/videointelligence/samples/analyze/video_detect_faces_beta.py
index 9d882d488e94..6e6a11f4d428 100644
--- a/videointelligence/samples/analyze/video_detect_faces_beta.py
+++ b/videointelligence/samples/analyze/video_detect_faces_beta.py
@@ -27,16 +27,18 @@ def detect_faces(local_file_path="path/to/your/video-file.mp4"):
         input_content = f.read()
 
     # Configure the request
-    config = videointelligence.types.FaceDetectionConfig(
+    config = videointelligence.FaceDetectionConfig(
         include_bounding_boxes=True, include_attributes=True
     )
-    context = videointelligence.types.VideoContext(face_detection_config=config)
+    context = videointelligence.VideoContext(face_detection_config=config)
 
     # Start the asynchronous request
     operation = client.annotate_video(
-        input_content=input_content,
-        features=[videointelligence.enums.Feature.FACE_DETECTION],
-        video_context=context,
+        request={
+            "features": [videointelligence.Feature.FACE_DETECTION],
+            "input_content": input_content,
+            "video_context": context,
+        }
     )
 
     print("\nProcessing video for face detection annotations.")
@@ -53,9 +55,9 @@ def detect_faces(local_file_path="path/to/your/video-file.mp4"):
             print(
                 "Segment: {}s to {}s".format(
                     track.segment.start_time_offset.seconds
-                    + track.segment.start_time_offset.nanos / 1e9,
+                    + track.segment.start_time_offset.microseconds / 1e6,
                     track.segment.end_time_offset.seconds
-                    + track.segment.end_time_offset.nanos / 1e9,
+                    + track.segment.end_time_offset.microseconds / 1e6,
                 )
             )
 
diff --git a/videointelligence/samples/analyze/video_detect_faces_gcs_beta.py b/videointelligence/samples/analyze/video_detect_faces_gcs_beta.py
index 262184b673ab..6646d6a54e33 100644
--- a/videointelligence/samples/analyze/video_detect_faces_gcs_beta.py
+++ b/videointelligence/samples/analyze/video_detect_faces_gcs_beta.py
@@ -22,16 +22,18 @@ def detect_faces(gcs_uri="gs://YOUR_BUCKET_ID/path/to/your/video.mp4"):
     client = videointelligence.VideoIntelligenceServiceClient()
 
     # Configure the request
-    config = videointelligence.types.FaceDetectionConfig(
+    config = videointelligence.FaceDetectionConfig(
         include_bounding_boxes=True, include_attributes=True
     )
-    context = videointelligence.types.VideoContext(face_detection_config=config)
+    context = videointelligence.VideoContext(face_detection_config=config)
 
     # Start the asynchronous request
     operation = client.annotate_video(
-        input_uri=gcs_uri,
-        features=[videointelligence.enums.Feature.FACE_DETECTION],
-        video_context=context,
+        request={
+            "features": [videointelligence.Feature.FACE_DETECTION],
+            "input_uri": gcs_uri,
+            "video_context": context,
+        }
     )
 
     print("\nProcessing video for face detection annotations.")
@@ -48,9 +50,9 @@ def detect_faces(gcs_uri="gs://YOUR_BUCKET_ID/path/to/your/video.mp4"):
             print(
                 "Segment: {}s to {}s".format(
                     track.segment.start_time_offset.seconds
-                    + track.segment.start_time_offset.nanos / 1e9,
+                    + track.segment.start_time_offset.microseconds / 1e6,
                     track.segment.end_time_offset.seconds
-                    + track.segment.end_time_offset.nanos / 1e9,
+                    + track.segment.end_time_offset.microseconds / 1e6,
                 )
             )
 
diff --git a/videointelligence/samples/analyze/video_detect_logo.py b/videointelligence/samples/analyze/video_detect_logo.py
index 319e1c8745a8..fbd0018f0c0b 100644
--- a/videointelligence/samples/analyze/video_detect_logo.py
+++ b/videointelligence/samples/analyze/video_detect_logo.py
@@ -26,9 +26,11 @@ def detect_logo(local_file_path="path/to/your/video.mp4"):
 
     with io.open(local_file_path, "rb") as f:
         input_content = f.read()
-    features = [videointelligence.enums.Feature.LOGO_RECOGNITION]
+    features = [videointelligence.Feature.LOGO_RECOGNITION]
 
-    operation = client.annotate_video(input_content=input_content, features=features)
+    operation = client.annotate_video(
+        request={"features": features, "input_content": input_content}
+    )
 
     print(u"Waiting for operation to complete...")
     response = operation.result()
@@ -53,13 +55,13 @@ def detect_logo(local_file_path="path/to/your/video.mp4"):
             print(
                 u"\n\tStart Time Offset : {}.{}".format(
                     track.segment.start_time_offset.seconds,
-                    track.segment.start_time_offset.nanos,
+                    track.segment.start_time_offset.microseconds * 1000,
                 )
             )
             print(
                 u"\tEnd Time Offset : {}.{}".format(
                     track.segment.end_time_offset.seconds,
-                    track.segment.end_time_offset.nanos,
+                    track.segment.end_time_offset.microseconds * 1000,
                 )
             )
             print(u"\tConfidence : {}".format(track.confidence))
@@ -91,12 +93,14 @@ def detect_logo(local_file_path="path/to/your/video.mp4"):
         for segment in logo_recognition_annotation.segments:
             print(
                 u"\n\tStart Time Offset : {}.{}".format(
-                    segment.start_time_offset.seconds, segment.start_time_offset.nanos,
+                    segment.start_time_offset.seconds,
+                    segment.start_time_offset.microseconds * 1000,
                 )
             )
             print(
                 u"\tEnd Time Offset : {}.{}".format(
-                    segment.end_time_offset.seconds, segment.end_time_offset.nanos,
+                    segment.end_time_offset.seconds,
+                    segment.end_time_offset.microseconds * 1000,
                 )
             )
 
diff --git a/videointelligence/samples/analyze/video_detect_logo_gcs.py b/videointelligence/samples/analyze/video_detect_logo_gcs.py
index a999888555f3..68db2e8bb634 100644
--- a/videointelligence/samples/analyze/video_detect_logo_gcs.py
+++ b/videointelligence/samples/analyze/video_detect_logo_gcs.py
@@ -21,9 +21,11 @@ def detect_logo_gcs(input_uri="gs://YOUR_BUCKET_ID/path/to/your/file.mp4"):
 
     client = videointelligence.VideoIntelligenceServiceClient()
 
-    features = [videointelligence.enums.Feature.LOGO_RECOGNITION]
+    features = [videointelligence.Feature.LOGO_RECOGNITION]
 
-    operation = client.annotate_video(input_uri=input_uri, features=features)
+    operation = client.annotate_video(
+        request={"features": features, "input_uri": input_uri}
+    )
 
     print(u"Waiting for operation to complete...")
     response = operation.result()
@@ -49,13 +51,13 @@ def detect_logo_gcs(input_uri="gs://YOUR_BUCKET_ID/path/to/your/file.mp4"):
             print(
                 u"\n\tStart Time Offset : {}.{}".format(
                     track.segment.start_time_offset.seconds,
-                    track.segment.start_time_offset.nanos,
+                    track.segment.start_time_offset.microseconds * 1000,
                 )
             )
             print(
                 u"\tEnd Time Offset : {}.{}".format(
                     track.segment.end_time_offset.seconds,
-                    track.segment.end_time_offset.nanos,
+                    track.segment.end_time_offset.microseconds * 1000,
                 )
             )
             print(u"\tConfidence : {}".format(track.confidence))
@@ -86,12 +88,14 @@ def detect_logo_gcs(input_uri="gs://YOUR_BUCKET_ID/path/to/your/file.mp4"):
         for segment in logo_recognition_annotation.segments:
             print(
                 u"\n\tStart Time Offset : {}.{}".format(
-                    segment.start_time_offset.seconds, segment.start_time_offset.nanos,
+                    segment.start_time_offset.seconds,
+                    segment.start_time_offset.microseconds * 1000,
                 )
             )
             print(
                 u"\tEnd Time Offset : {}.{}".format(
-                    segment.end_time_offset.seconds, segment.end_time_offset.nanos,
+                    segment.end_time_offset.seconds,
+                    segment.end_time_offset.microseconds * 1000,
                 )
             )
 
diff --git a/videointelligence/samples/analyze/video_detect_person_beta.py b/videointelligence/samples/analyze/video_detect_person_beta.py
index a7afd34cab49..143d1b7d1514 100644
--- a/videointelligence/samples/analyze/video_detect_person_beta.py
+++ b/videointelligence/samples/analyze/video_detect_person_beta.py
@@ -36,9 +36,11 @@ def detect_person(local_file_path="path/to/your/video-file.mp4"):
 
     # Start the asynchronous request
     operation = client.annotate_video(
-        input_content=input_content,
-        features=[videointelligence.enums.Feature.PERSON_DETECTION],
-        video_context=context,
+        request={
+            "features": [videointelligence.Feature.PERSON_DETECTION],
+            "input_content": input_content,
+            "video_context": context,
+        }
     )
 
     print("\nProcessing video for person detection annotations.")
@@ -55,9 +57,9 @@ def detect_person(local_file_path="path/to/your/video-file.mp4"):
             print(
                 "Segment: {}s to {}s".format(
                     track.segment.start_time_offset.seconds
-                    + track.segment.start_time_offset.nanos / 1e9,
+                    + track.segment.start_time_offset.microseconds / 1e6,
                     track.segment.end_time_offset.seconds
-                    + track.segment.end_time_offset.nanos / 1e9,
+                    + track.segment.end_time_offset.microseconds / 1e6,
                 )
             )
 
diff --git a/videointelligence/samples/analyze/video_detect_person_gcs_beta.py b/videointelligence/samples/analyze/video_detect_person_gcs_beta.py
index 9bd2d007b73f..7496fd254ca6 100644
--- a/videointelligence/samples/analyze/video_detect_person_gcs_beta.py
+++ b/videointelligence/samples/analyze/video_detect_person_gcs_beta.py
@@ -31,9 +31,11 @@ def detect_person(gcs_uri="gs://YOUR_BUCKET_ID/path/to/your/video.mp4"):
 
     # Start the asynchronous request
     operation = client.annotate_video(
-        input_uri=gcs_uri,
-        features=[videointelligence.enums.Feature.PERSON_DETECTION],
-        video_context=context,
+        request={
+            "features": [videointelligence.Feature.PERSON_DETECTION],
+            "input_uri": gcs_uri,
+            "video_context": context,
+        }
     )
 
     print("\nProcessing video for person detection annotations.")
@@ -50,9 +52,9 @@ def detect_person(gcs_uri="gs://YOUR_BUCKET_ID/path/to/your/video.mp4"):
             print(
                 "Segment: {}s to {}s".format(
                     track.segment.start_time_offset.seconds
-                    + track.segment.start_time_offset.nanos / 1e9,
+                    + track.segment.start_time_offset.microseconds / 1e6,
                     track.segment.end_time_offset.seconds
-                    + track.segment.end_time_offset.nanos / 1e9,
+                    + track.segment.end_time_offset.microseconds / 1e6,
                 )
             )
 
diff --git a/videointelligence/samples/labels/labels.py b/videointelligence/samples/labels/labels.py
index cfb4ad0c4259..8f8c31ddd0c1 100644
--- a/videointelligence/samples/labels/labels.py
+++ b/videointelligence/samples/labels/labels.py
@@ -32,6 +32,7 @@
 import argparse
 
 from google.cloud import videointelligence
+
 # [END video_label_tutorial_imports]
 
 
@@ -39,44 +40,50 @@ def analyze_labels(path):
     """ Detects labels given a GCS path. """
     # [START video_label_tutorial_construct_request]
     video_client = videointelligence.VideoIntelligenceServiceClient()
-    features = [videointelligence.enums.Feature.LABEL_DETECTION]
-    operation = video_client.annotate_video(input_uri=path, features=features)
+    features = [videointelligence.Feature.LABEL_DETECTION]
+    operation = video_client.annotate_video(
+        request={"features": features, "input_uri": path}
+    )
     # [END video_label_tutorial_construct_request]
-    print('\nProcessing video for label annotations:')
+    print("\nProcessing video for label annotations:")
 
     # [START video_label_tutorial_check_operation]
     result = operation.result(timeout=90)
-    print('\nFinished processing.')
+    print("\nFinished processing.")
     # [END video_label_tutorial_check_operation]
 
     # [START video_label_tutorial_parse_response]
     segment_labels = result.annotation_results[0].segment_label_annotations
     for i, segment_label in enumerate(segment_labels):
-        print('Video label description: {}'.format(
-            segment_label.entity.description))
+        print("Video label description: {}".format(segment_label.entity.description))
         for category_entity in segment_label.category_entities:
-            print('\tLabel category description: {}'.format(
-                category_entity.description))
+            print(
+                "\tLabel category description: {}".format(category_entity.description)
+            )
 
         for i, segment in enumerate(segment_label.segments):
-            start_time = (segment.segment.start_time_offset.seconds +
-                          segment.segment.start_time_offset.nanos / 1e9)
-            end_time = (segment.segment.end_time_offset.seconds +
-                        segment.segment.end_time_offset.nanos / 1e9)
-            positions = '{}s to {}s'.format(start_time, end_time)
+            start_time = (
+                segment.segment.start_time_offset.seconds
+                + segment.segment.start_time_offset.microseconds / 1e6
+            )
+            end_time = (
+                segment.segment.end_time_offset.seconds
+                + segment.segment.end_time_offset.microseconds / 1e6
+            )
+            positions = "{}s to {}s".format(start_time, end_time)
             confidence = segment.confidence
-            print('\tSegment {}: {}'.format(i, positions))
-            print('\tConfidence: {}'.format(confidence))
-        print('\n')
+            print("\tSegment {}: {}".format(i, positions))
+            print("\tConfidence: {}".format(confidence))
+        print("\n")
     # [END video_label_tutorial_parse_response]
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     # [START video_label_tutorial_run_application]
     parser = argparse.ArgumentParser(
-        description=__doc__,
-        formatter_class=argparse.RawDescriptionHelpFormatter)
-    parser.add_argument('path', help='GCS file path for label detection.')
+        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
+    )
+    parser.add_argument("path", help="GCS file path for label detection.")
     args = parser.parse_args()
 
     analyze_labels(args.path)
diff --git a/videointelligence/samples/labels/labels_test.py b/videointelligence/samples/labels/labels_test.py
index 1249f5b95cbe..c678ddb43adc 100644
--- a/videointelligence/samples/labels/labels_test.py
+++ b/videointelligence/samples/labels/labels_test.py
@@ -21,6 +21,6 @@
 
 @pytest.mark.slow
 def test_feline_video_labels(capsys):
-    labels.analyze_labels('gs://cloud-samples-data/video/cat.mp4')
+    labels.analyze_labels("gs://cloud-samples-data/video/cat.mp4")
     out, _ = capsys.readouterr()
-    assert 'Video label description: cat' in out
+    assert "Video label description: cat" in out
diff --git a/videointelligence/samples/labels/noxfile.py b/videointelligence/samples/labels/noxfile.py
index b90eef00f2d9..ab2c49227c3b 100644
--- a/videointelligence/samples/labels/noxfile.py
+++ b/videointelligence/samples/labels/noxfile.py
@@ -37,28 +37,25 @@
 
 TEST_CONFIG = {
     # You can opt out from the test for specific Python versions.
-    'ignored_versions': ["2.7"],
-
+    "ignored_versions": ["2.7"],
     # Old samples are opted out of enforcing Python type hints
     # All new samples should feature them
-    'enforce_type_hints': False,
-
+    "enforce_type_hints": False,
     # An envvar key for determining the project id to use. Change it
     # to 'BUILD_SPECIFIC_GCLOUD_PROJECT' if you want to opt in using a
     # build specific Cloud project. You can also use your own string
     # to use your own Cloud project.
-    'gcloud_project_env': 'GOOGLE_CLOUD_PROJECT',
+    "gcloud_project_env": "GOOGLE_CLOUD_PROJECT",
     # 'gcloud_project_env': 'BUILD_SPECIFIC_GCLOUD_PROJECT',
-
     # A dictionary you want to inject into your test. Don't put any
     # secrets here. These values will override predefined values.
-    'envs': {},
+    "envs": {},
 }
 
 
 try:
     # Ensure we can import noxfile_config in the project's directory.
-    sys.path.append('.')
+    sys.path.append(".")
     from noxfile_config import TEST_CONFIG_OVERRIDE
 except ImportError as e:
     print("No user noxfile_config found: detail: {}".format(e))
@@ -73,12 +70,12 @@ def get_pytest_env_vars():
     ret = {}
 
     # Override the GCLOUD_PROJECT and the alias.
-    env_key = TEST_CONFIG['gcloud_project_env']
+    env_key = TEST_CONFIG["gcloud_project_env"]
     # This should error out if not set.
-    ret['GOOGLE_CLOUD_PROJECT'] = os.environ[env_key]
+    ret["GOOGLE_CLOUD_PROJECT"] = os.environ[env_key]
 
     # Apply user supplied envs.
-    ret.update(TEST_CONFIG['envs'])
+    ret.update(TEST_CONFIG["envs"])
     return ret
 
 
@@ -87,7 +84,7 @@ def get_pytest_env_vars():
 ALL_VERSIONS = ["2.7", "3.6", "3.7", "3.8"]
 
 # Any default versions that should be ignored.
-IGNORED_VERSIONS = TEST_CONFIG['ignored_versions']
+IGNORED_VERSIONS = TEST_CONFIG["ignored_versions"]
 
 TESTED_VERSIONS = sorted([v for v in ALL_VERSIONS if v not in IGNORED_VERSIONS])
 
@@ -136,7 +133,7 @@ def _determine_local_import_names(start_dir):
 
 @nox.session
 def lint(session):
-    if not TEST_CONFIG['enforce_type_hints']:
+    if not TEST_CONFIG["enforce_type_hints"]:
         session.install("flake8", "flake8-import-order")
     else:
         session.install("flake8", "flake8-import-order", "flake8-annotations")
@@ -145,9 +142,11 @@ def lint(session):
     args = FLAKE8_COMMON_ARGS + [
         "--application-import-names",
         ",".join(local_names),
-        "."
+        ".",
     ]
     session.run("flake8", *args)
+
+
 #
 # Black
 #
@@ -160,6 +159,7 @@ def blacken(session):
 
     session.run("black", *python_files)
 
+
 #
 # Sample Tests
 #
@@ -199,9 +199,9 @@ def py(session):
     if session.python in TESTED_VERSIONS:
         _session_tests(session)
     else:
-        session.skip("SKIPPED: {} tests are disabled for this sample.".format(
-            session.python
-        ))
+        session.skip(
+            "SKIPPED: {} tests are disabled for this sample.".format(session.python)
+        )
 
 
 #
diff --git a/videointelligence/samples/quickstart/noxfile.py b/videointelligence/samples/quickstart/noxfile.py
index b90eef00f2d9..ab2c49227c3b 100644
--- a/videointelligence/samples/quickstart/noxfile.py
+++ b/videointelligence/samples/quickstart/noxfile.py
@@ -37,28 +37,25 @@
 
 TEST_CONFIG = {
     # You can opt out from the test for specific Python versions.
-    'ignored_versions': ["2.7"],
-
+    "ignored_versions": ["2.7"],
     # Old samples are opted out of enforcing Python type hints
     # All new samples should feature them
-    'enforce_type_hints': False,
-
+    "enforce_type_hints": False,
     # An envvar key for determining the project id to use. Change it
     # to 'BUILD_SPECIFIC_GCLOUD_PROJECT' if you want to opt in using a
     # build specific Cloud project. You can also use your own string
     # to use your own Cloud project.
-    'gcloud_project_env': 'GOOGLE_CLOUD_PROJECT',
+    "gcloud_project_env": "GOOGLE_CLOUD_PROJECT",
     # 'gcloud_project_env': 'BUILD_SPECIFIC_GCLOUD_PROJECT',
-
     # A dictionary you want to inject into your test. Don't put any
     # secrets here. These values will override predefined values.
-    'envs': {},
+    "envs": {},
 }
 
 
 try:
     # Ensure we can import noxfile_config in the project's directory.
-    sys.path.append('.')
+    sys.path.append(".")
     from noxfile_config import TEST_CONFIG_OVERRIDE
 except ImportError as e:
     print("No user noxfile_config found: detail: {}".format(e))
@@ -73,12 +70,12 @@ def get_pytest_env_vars():
     ret = {}
 
     # Override the GCLOUD_PROJECT and the alias.
-    env_key = TEST_CONFIG['gcloud_project_env']
+    env_key = TEST_CONFIG["gcloud_project_env"]
     # This should error out if not set.
-    ret['GOOGLE_CLOUD_PROJECT'] = os.environ[env_key]
+    ret["GOOGLE_CLOUD_PROJECT"] = os.environ[env_key]
 
     # Apply user supplied envs.
-    ret.update(TEST_CONFIG['envs'])
+    ret.update(TEST_CONFIG["envs"])
     return ret
 
 
@@ -87,7 +84,7 @@ def get_pytest_env_vars():
 ALL_VERSIONS = ["2.7", "3.6", "3.7", "3.8"]
 
 # Any default versions that should be ignored.
-IGNORED_VERSIONS = TEST_CONFIG['ignored_versions']
+IGNORED_VERSIONS = TEST_CONFIG["ignored_versions"]
 
 TESTED_VERSIONS = sorted([v for v in ALL_VERSIONS if v not in IGNORED_VERSIONS])
 
@@ -136,7 +133,7 @@ def _determine_local_import_names(start_dir):
 
 @nox.session
 def lint(session):
-    if not TEST_CONFIG['enforce_type_hints']:
+    if not TEST_CONFIG["enforce_type_hints"]:
         session.install("flake8", "flake8-import-order")
     else:
         session.install("flake8", "flake8-import-order", "flake8-annotations")
@@ -145,9 +142,11 @@ def lint(session):
     args = FLAKE8_COMMON_ARGS + [
         "--application-import-names",
         ",".join(local_names),
-        "."
+        ".",
     ]
     session.run("flake8", *args)
+
+
 #
 # Black
 #
@@ -160,6 +159,7 @@ def blacken(session):
 
     session.run("black", *python_files)
 
+
 #
 # Sample Tests
 #
@@ -199,9 +199,9 @@ def py(session):
     if session.python in TESTED_VERSIONS:
         _session_tests(session)
     else:
-        session.skip("SKIPPED: {} tests are disabled for this sample.".format(
-            session.python
-        ))
+        session.skip(
+            "SKIPPED: {} tests are disabled for this sample.".format(session.python)
+        )
 
 
 #
diff --git a/videointelligence/samples/quickstart/quickstart.py b/videointelligence/samples/quickstart/quickstart.py
index 19d126f01cb3..89f75d866825 100644
--- a/videointelligence/samples/quickstart/quickstart.py
+++ b/videointelligence/samples/quickstart/quickstart.py
@@ -28,9 +28,12 @@ def run_quickstart():
     from google.cloud import videointelligence
 
     video_client = videointelligence.VideoIntelligenceServiceClient()
-    features = [videointelligence.enums.Feature.LABEL_DETECTION]
+    features = [videointelligence.Feature.LABEL_DETECTION]
     operation = video_client.annotate_video(
-        "gs://cloud-samples-data/video/cat.mp4", features=features
+        request={
+            "features": features,
+            "input_uri": "gs://cloud-samples-data/video/cat.mp4",
+        }
     )
     print("\nProcessing video for label annotations:")
 
@@ -49,11 +52,11 @@ def run_quickstart():
         for i, segment in enumerate(segment_label.segments):
             start_time = (
                 segment.segment.start_time_offset.seconds
-                + segment.segment.start_time_offset.nanos / 1e9
+                + segment.segment.start_time_offset.microseconds / 1e6
             )
             end_time = (
                 segment.segment.end_time_offset.seconds
-                + segment.segment.end_time_offset.nanos / 1e9
+                + segment.segment.end_time_offset.microseconds / 1e6
             )
             positions = "{}s to {}s".format(start_time, end_time)
             confidence = segment.confidence
diff --git a/videointelligence/samples/shotchange/noxfile.py b/videointelligence/samples/shotchange/noxfile.py
index b90eef00f2d9..e38c11b7a7e2 100644
--- a/videointelligence/samples/shotchange/noxfile.py
+++ b/videointelligence/samples/shotchange/noxfile.py
@@ -37,28 +37,25 @@
 
 TEST_CONFIG = {
     # You can opt out from the test for specific Python versions.
-    'ignored_versions': ["2.7"],
-
+    "ignored_versions": ["2.7"],
     # Old samples are opted out of enforcing Python type hints
     # All new samples should feature them
-    'enforce_type_hints': False,
-
+    "enforce_type_hints": False,
     # An envvar key for determining the project id to use. Change it
     # to 'BUILD_SPECIFIC_GCLOUD_PROJECT' if you want to opt in using a
     # build specific Cloud project. You can also use your own string
     # to use your own Cloud project.
-    'gcloud_project_env': 'GOOGLE_CLOUD_PROJECT',
+    "gcloud_project_env": "GOOGLE_CLOUD_PROJECT",
     # 'gcloud_project_env': 'BUILD_SPECIFIC_GCLOUD_PROJECT',
-
     # A dictionary you want to inject into your test. Don't put any
     # secrets here. These values will override predefined values.
-    'envs': {},
+    "envs": {},
 }
 
 
 try:
     # Ensure we can import noxfile_config in the project's directory.
-    sys.path.append('.')
+    sys.path.append(".")
     from noxfile_config import TEST_CONFIG_OVERRIDE
 except ImportError as e:
     print("No user noxfile_config found: detail: {}".format(e))
@@ -73,12 +70,13 @@ def get_pytest_env_vars():
     ret = {}
 
     # Override the GCLOUD_PROJECT and the alias.
-    env_key = TEST_CONFIG['gcloud_project_env']
+    env_key = TEST_CONFIG["gcloud_project_env"]
     # This should error out if not set.
-    ret['GOOGLE_CLOUD_PROJECT'] = os.environ[env_key]
+    ret["GOOGLE_CLOUD_PROJECT"] = os.environ[env_key]
 
     # Apply user supplied envs.
-    ret.update(TEST_CONFIG['envs'])
+    ret.update(TEST_CONFIG["envs"])
+
     return ret
 
 
@@ -87,7 +85,7 @@ def get_pytest_env_vars():
 ALL_VERSIONS = ["2.7", "3.6", "3.7", "3.8"]
 
 # Any default versions that should be ignored.
-IGNORED_VERSIONS = TEST_CONFIG['ignored_versions']
+IGNORED_VERSIONS = TEST_CONFIG["ignored_versions"]
 
 TESTED_VERSIONS = sorted([v for v in ALL_VERSIONS if v not in IGNORED_VERSIONS])
 
@@ -136,7 +134,7 @@ def _determine_local_import_names(start_dir):
 
 @nox.session
 def lint(session):
-    if not TEST_CONFIG['enforce_type_hints']:
+    if not TEST_CONFIG["enforce_type_hints"]:
         session.install("flake8", "flake8-import-order")
     else:
         session.install("flake8", "flake8-import-order", "flake8-annotations")
@@ -145,9 +143,11 @@ def lint(session):
     args = FLAKE8_COMMON_ARGS + [
         "--application-import-names",
         ",".join(local_names),
-        "."
+        ".",
     ]
     session.run("flake8", *args)
+
+
 #
 # Black
 #
@@ -160,6 +160,7 @@ def blacken(session):
 
     session.run("black", *python_files)
 
+
 #
 # Sample Tests
 #
@@ -199,9 +200,9 @@ def py(session):
     if session.python in TESTED_VERSIONS:
         _session_tests(session)
     else:
-        session.skip("SKIPPED: {} tests are disabled for this sample.".format(
-            session.python
-        ))
+        session.skip(
+            "SKIPPED: {} tests are disabled for this sample.".format(session.python)
+        )
 
 
 #
diff --git a/videointelligence/samples/shotchange/shotchange.py b/videointelligence/samples/shotchange/shotchange.py
index 40edf0012cec..241289f96a62 100644
--- a/videointelligence/samples/shotchange/shotchange.py
+++ b/videointelligence/samples/shotchange/shotchange.py
@@ -31,6 +31,7 @@
 import argparse
 
 from google.cloud import videointelligence
+
 # [END video_shot_tutorial_imports]
 
 
@@ -38,32 +39,37 @@ def analyze_shots(path):
     """ Detects camera shot changes. """
     # [START video_shot_tutorial_construct_request]
     video_client = videointelligence.VideoIntelligenceServiceClient()
-    features = [videointelligence.enums.Feature.SHOT_CHANGE_DETECTION]
-    operation = video_client.annotate_video(input_uri=path, features=features)
+    features = [videointelligence.Feature.SHOT_CHANGE_DETECTION]
+    operation = video_client.annotate_video(
+        request={"features": features, "input_uri": path}
+    )
     # [END video_shot_tutorial_construct_request]
-    print('\nProcessing video for shot change annotations:')
+    print("\nProcessing video for shot change annotations:")
 
     # [START video_shot_tutorial_check_operation]
     result = operation.result(timeout=120)
-    print('\nFinished processing.')
+    print("\nFinished processing.")
+
     # [END video_shot_tutorial_check_operation]
 
     # [START video_shot_tutorial_parse_response]
     for i, shot in enumerate(result.annotation_results[0].shot_annotations):
-        start_time = (shot.start_time_offset.seconds +
-                      shot.start_time_offset.nanos / 1e9)
-        end_time = (shot.end_time_offset.seconds +
-                    shot.end_time_offset.nanos / 1e9)
-        print('\tShot {}: {} to {}'.format(i, start_time, end_time))
+        start_time = (
+            shot.start_time_offset.seconds + shot.start_time_offset.microseconds / 1e6
+        )
+        end_time = (
+            shot.end_time_offset.seconds + shot.end_time_offset.microseconds / 1e6
+        )
+        print("\tShot {}: {} to {}".format(i, start_time, end_time))
     # [END video_shot_tutorial_parse_response]
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     # [START video_shot_tutorial_run_application]
     parser = argparse.ArgumentParser(
-        description=__doc__,
-        formatter_class=argparse.RawDescriptionHelpFormatter)
-    parser.add_argument('path', help='GCS path for shot change detection.')
+        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
+    )
+    parser.add_argument("path", help="GCS path for shot change detection.")
     args = parser.parse_args()
 
     analyze_shots(args.path)
diff --git a/videointelligence/samples/shotchange/shotchange_test.py b/videointelligence/samples/shotchange/shotchange_test.py
index 0722e0c66aaf..bb480351673d 100644
--- a/videointelligence/samples/shotchange/shotchange_test.py
+++ b/videointelligence/samples/shotchange/shotchange_test.py
@@ -21,7 +21,6 @@
 
 @pytest.mark.slow
 def test_shots_dino(capsys):
-    shotchange.analyze_shots(
-        'gs://cloud-samples-data/video/gbikes_dinosaur.mp4')
+    shotchange.analyze_shots("gs://cloud-samples-data/video/gbikes_dinosaur.mp4")
     out, _ = capsys.readouterr()
-    assert 'Shot 1:' in out
+    assert "Shot 1:" in out