chore(python): run blacken session for all directories with a noxfile (#220)

Source-Link: googleapis/synthtool@bc0de6e
Post-Processor: gcr.io/cloud-devrel-public-resources/owlbot-python:latest@sha256:39ad8c0570e4f5d2d3124a509de4fe975e799e2b97e0f58aed88f8880d5a8b60

Co-authored-by: Owl Bot <gcf-owl-bot[bot]@users.noreply.github.com>

Author: gcf-owl-bot[bot]

language/snippets/classify_text/classify_text_tutorial.py

@@ -42,7 +42,7 @@ def classify(text, verbose=True):
     document = language_v1.Document(
         content=text, type_=language_v1.Document.Type.PLAIN_TEXT
     )
-    response = language_client.classify_text(request={'document': document})
+    response = language_client.classify_text(request={"document": document})
     categories = response.categories
 
     result = {}

language/snippets/cloud-client/v1/quickstart.py

@@ -30,10 +30,14 @@ def run_quickstart():
 
     # The text to analyze
     text = u"Hello, world!"
-    document = language_v1.Document(content=text, type_=language_v1.Document.Type.PLAIN_TEXT)
+    document = language_v1.Document(
+        content=text, type_=language_v1.Document.Type.PLAIN_TEXT
+    )
 
     # Detects the sentiment of the text
-    sentiment = client.analyze_sentiment(request={'document': document}).document_sentiment
+    sentiment = client.analyze_sentiment(
+        request={"document": document}
+    ).document_sentiment
 
     print("Text: {}".format(text))
     print("Sentiment: {}, {}".format(sentiment.score, sentiment.magnitude))

language/snippets/cloud-client/v1/set_endpoint.py

@@ -31,7 +31,9 @@ def set_endpoint():
     )
 
     # Detects the sentiment of the text
-    sentiment = client.analyze_sentiment(request={'document': document}).document_sentiment
+    sentiment = client.analyze_sentiment(
+        request={"document": document}
+    ).document_sentiment
 
     print("Sentiment: {}, {}".format(sentiment.score, sentiment.magnitude))
 

language/snippets/generated-samples/v1/language_sentiment_text.py

@@ -39,7 +39,7 @@ def sample_analyze_sentiment(content):
     type_ = language_v1.Document.Type.PLAIN_TEXT
     document = {"type_": type_, "content": content}
 
-    response = client.analyze_sentiment(request={'document': document})
+    response = client.analyze_sentiment(request={"document": document})
     sentiment = response.document_sentiment
     print("Score: {}".format(sentiment.score))
     print("Magnitude: {}".format(sentiment.magnitude))

language/snippets/sentiment/sentiment_analysis.py

@@ -51,8 +51,10 @@ def analyze(movie_review_filename):
         # Instantiates a plain text document.
         content = review_file.read()
 
-    document = language_v1.Document(content=content, type_=language_v1.Document.Type.PLAIN_TEXT)
-    annotations = client.analyze_sentiment(request={'document': document})
+    document = language_v1.Document(
+        content=content, type_=language_v1.Document.Type.PLAIN_TEXT
+    )
+    annotations = client.analyze_sentiment(request={"document": document})
 
     # Print the results
     print_result(annotations)