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diff --git a/‎vision/automl/automl_vision_batch_predict.py
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+# -*- coding: utf-8 -*-
+#
+# Copyright 2019 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# DO NOT EDIT! This is a generated sample ("LongRunningPromise",  "automl_vision_batch_predict")
+
+# To install the latest published package dependency, execute the following:
+#   pip install google-cloud-automl
+
+# sample-metadata
+#   title: AutoML Batch Predict (AutoML Vision)
+#   description: AutoML Batch Predict using AutoML Vision
+#   usage: python3 samples/v1beta1/automl_vision_batch_predict.py [--input_uri "gs://[BUCKET-NAME]/path/to/file-with-image-urls.csv"] [--output_uri "gs://[BUCKET-NAME]/directory-for-output-files/"] [--project "[Google Cloud Project ID]"] [--model_id "[Model ID]"]
+
+# [START automl_vision_batch_predict]
+from google.cloud import automl_v1beta1
+
+
+def sample_batch_predict(input_uri, output_uri, project, model_id):
+    """
+    AutoML Batch Predict using AutoML Vision
+
+    Args:
+      input_uri Google Cloud Storage URI to CSV file in your bucket that contains the
+      paths to the images to annotate, e.g. gs://[BUCKET-NAME]/path/to/images.csv
+      Each line specifies a separate path to an image in Google Cloud Storage.
+      output_uri Identifies where to store the output of your prediction request
+      in your Google Cloud Storage bucket.
+      You must have write permissions to the Google Cloud Storage bucket.
+      project Required. Your Google Cloud Project ID.
+      model_id Model ID, e.g. VOT1234567890123456789
+    """
+
+    client = automl_v1beta1.PredictionServiceClient()
+
+    # input_uri = 'gs://[BUCKET-NAME]/path/to/file-with-image-urls.csv'
+    # output_uri = 'gs://[BUCKET-NAME]/directory-for-output-files/'
+    # project = '[Google Cloud Project ID]'
+    # model_id = '[Model ID]'
+    name = client.model_path(project, "us-central1", model_id)
+    input_uris = [input_uri]
+    gcs_source = {"input_uris": input_uris}
+    input_config = {"gcs_source": gcs_source}
+    gcs_destination = {"output_uri_prefix": output_uri}
+    output_config = {"gcs_destination": gcs_destination}
+
+    # A value from 0.0 to 1.0. When the model detects objects on video frames,
+    # it will only produce bounding boxes that have at least this confidence score.
+    # The default is 0.5.
+    params_item = "0.0"
+    params = {"score_threshold": params_item}
+
+    operation = client.batch_predict(name, input_config, output_config, params=params)
+
+    print(u"Waiting for operation to complete...")
+    response = operation.result()
+
+    print(u"Batch Prediction results saved to specified Cloud Storage bucket.")
+
+
+# [END automl_vision_batch_predict]
+
+
+def main():
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--input_uri",
+        type=str,
+        default="gs://[BUCKET-NAME]/path/to/file-with-image-urls.csv",
+    )
+    parser.add_argument(
+        "--output_uri",
+        type=str,
+        default="gs://[BUCKET-NAME]/directory-for-output-files/",
+    )
+    parser.add_argument("--project", type=str, default="[Google Cloud Project ID]")
+    parser.add_argument("--model_id", type=str, default="[Model ID]")
+    args = parser.parse_args()
+
+    sample_batch_predict(args.input_uri, args.output_uri, args.project, args.model_id)
+
+
+if __name__ == "__main__":
+    main()