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Cloud Natural Language API Python script error (Client object has no attribute create_rows)

I was trying to create a script that feeds articles through the classification tool of the natural language api and I found a tutorial that does exatcly that. I was following this simple tutorial to get an intro into google cloud and the natural language api.

The end result is supposed to be a script that sends a bunch of news articles from the google cloud storage to the natural language api to classify the articles and the save the whole thing into a table created in bigquery.

I was following the example fine, but when running the final script I get the following error:

Traceback (most recent call last):
File "classify-text.py", line 39, in <module>
errors = bq_client.create_rows(table, rows_for_bq)
AttributeError: 'Client' object has no attribute 'create_rows'


The full script is:

from google.cloud import storage, language, bigquery

# Set up our GCS, NL, and BigQuery clients
storage_client = storage.Client()
nl_client = language.LanguageServiceClient()
# TODO: replace YOUR_PROJECT with your project name below
bq_client = bigquery.Client(project='Your_Project')

dataset_ref = bq_client.dataset('news_classification')
dataset = bigquery.Dataset(dataset_ref)
table_ref = dataset.table('article_data')
table = bq_client.get_table(table_ref)

# Send article text to the NL API's classifyText method
def classify_text(article):
response = nl_client.classify_text(
document=language.types.Document(
content=article,
type=language.enums.Document.Type.PLAIN_TEXT
)
)
return response


rows_for_bq =
files = storage_client.bucket('text-classification-codelab').list_blobs()
print("Got article files from GCS, sending them to the NL API (this will take ~2 minutes)...")

# Send files to the NL API and save the result to send to BigQuery
for file in files:
if file.name.endswith('txt'):
article_text = file.download_as_string()
nl_response = classify_text(article_text)
if len(nl_response.categories) > 0:
rows_for_bq.append((article_text, nl_response.categories[0].name, nl_response.categories[0].confidence))

print("Writing NL API article data to BigQuery...")
# Write article text + category data to BQ
errors = bq_client.create_rows(table, rows_for_bq)
assert errors ==


create_rows()

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