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chore: use gapic-generator-python 0.63.2 (#333)
* chore: use gapic-generator-python 0.63.2 docs: add generated snippets PiperOrigin-RevId: 427792504 Source-Link: googleapis/googleapis@55b9e1e Source-Link: googleapis/googleapis-gen@bf4e86b Copy-Tag: eyJwIjoiLmdpdGh1Yi8uT3dsQm90LnlhbWwiLCJoIjoiYmY0ZTg2Yjc1M2Y0MmNiMGVkYjFmZDUxZmJlODQwZDdkYTBhMWNkZSJ9 * 🦉 Updates from OwlBot See https://github.com/googleapis/repo-automation-bots/blob/main/packages/owl-bot/README.md Co-authored-by: Owl Bot <gcf-owl-bot[bot]@users.noreply.github.com>
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‎packages/google-cloud-automl/google/cloud/automl_v1/services/auto_ml/async_client.py‎

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‎packages/google-cloud-automl/google/cloud/automl_v1/services/auto_ml/client.py‎

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‎packages/google-cloud-automl/google/cloud/automl_v1/services/prediction_service/async_client.py‎

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@@ -260,6 +260,30 @@ async def predict(
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- A row with column values matching the columns of the model,
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up to 5MB. Not available for FORECASTING ``prediction_type``.
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.. code-block::
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from google.cloud import automl_v1
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def sample_predict():
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# Create a client
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client = automl_v1.PredictionServiceClient()
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# Initialize request argument(s)
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payload = automl_v1.ExamplePayload()
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payload.image.image_bytes = b'image_bytes_blob'
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request = automl_v1.PredictRequest(
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name="name_value",
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payload=payload,
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)
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# Make the request
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response = client.predict(request=request)
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# Handle the response
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print(response)
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Args:
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request (Union[google.cloud.automl_v1.types.PredictRequest, dict]):
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The request object. Request message for
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- AutoML Natural Language Sentiment Analysis
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- AutoML Tables
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.. code-block::
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from google.cloud import automl_v1
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def sample_batch_predict():
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# Create a client
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client = automl_v1.PredictionServiceClient()
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# Initialize request argument(s)
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input_config = automl_v1.BatchPredictInputConfig()
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input_config.gcs_source.input_uris = ['input_uris_value_1', 'input_uris_value_2']
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output_config = automl_v1.BatchPredictOutputConfig()
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output_config.gcs_destination.output_uri_prefix = "output_uri_prefix_value"
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request = automl_v1.BatchPredictRequest(
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name="name_value",
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input_config=input_config,
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output_config=output_config,
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)
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# Make the request
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operation = client.batch_predict(request=request)
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print("Waiting for operation to complete...")
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response = operation.result()
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# Handle the response
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print(response)
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Args:
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request (Union[google.cloud.automl_v1.types.BatchPredictRequest, dict]):
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The request object. Request message for

‎packages/google-cloud-automl/google/cloud/automl_v1/services/prediction_service/client.py‎

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- A row with column values matching the columns of the model,
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up to 5MB. Not available for FORECASTING ``prediction_type``.
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.. code-block::
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from google.cloud import automl_v1
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def sample_predict():
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# Create a client
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client = automl_v1.PredictionServiceClient()
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# Initialize request argument(s)
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payload = automl_v1.ExamplePayload()
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payload.image.image_bytes = b'image_bytes_blob'
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request = automl_v1.PredictRequest(
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name="name_value",
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payload=payload,
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)
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# Make the request
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response = client.predict(request=request)
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# Handle the response
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print(response)
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Args:
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request (Union[google.cloud.automl_v1.types.PredictRequest, dict]):
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The request object. Request message for
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- AutoML Natural Language Sentiment Analysis
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- AutoML Tables
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.. code-block::
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from google.cloud import automl_v1
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def sample_batch_predict():
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# Create a client
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client = automl_v1.PredictionServiceClient()
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# Initialize request argument(s)
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input_config = automl_v1.BatchPredictInputConfig()
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input_config.gcs_source.input_uris = ['input_uris_value_1', 'input_uris_value_2']
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output_config = automl_v1.BatchPredictOutputConfig()
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output_config.gcs_destination.output_uri_prefix = "output_uri_prefix_value"
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request = automl_v1.BatchPredictRequest(
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name="name_value",
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input_config=input_config,
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output_config=output_config,
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)
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# Make the request
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operation = client.batch_predict(request=request)
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print("Waiting for operation to complete...")
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response = operation.result()
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# Handle the response
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print(response)
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Args:
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request (Union[google.cloud.automl_v1.types.BatchPredictRequest, dict]):
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The request object. Request message for

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