apache
diff --git a/‎airflow/providers/google/cloud/utils/mlengine_prediction_summary.py
Lines changed: 2 additions & 1 deletion b/‎airflow/providers/google/cloud/utils/mlengine_prediction_summary.py
Lines changed: 2 additions & 1 deletion
diff --git a/‎docs/apache-airflow-providers-google/operators/cloud/mlengine.rst
Lines changed: 15 additions & 15 deletions b/‎docs/apache-airflow-providers-google/operators/cloud/mlengine.rst
Lines changed: 15 additions & 15 deletions
diff --git a/‎tests/providers/google/cloud/operators/test_mlengine_system.py
Lines changed: 0 additions & 56 deletions b/‎tests/providers/google/cloud/operators/test_mlengine_system.py
Lines changed: 0 additions & 56 deletions
diff --git a/‎tests/system/providers/google/cloud/ml_engine/__init__.py
Lines changed: 16 additions & 0 deletions b/‎tests/system/providers/google/cloud/ml_engine/__init__.py
Lines changed: 16 additions & 0 deletions
@@ -116,9 +116,10 @@ def metric_fn(inst):
 
 import apache_beam as beam
 import dill
+from apache_beam.coders.coders import Coder
 
 
-class JsonCoder:
+class JsonCoder(Coder):
     """JSON encoder/decoder."""
 
     @staticmethod
 
@@ -40,7 +40,7 @@ This creates a virtual machine that can run code specified in the trainer file,
 contains the main application code. A job can be initiated with the
 :class:`~airflow.providers.google.cloud.operators.mlengine.MLEngineStartTrainingJobOperator`.
 
-.. exampleinclude:: /../../airflow/providers/google/cloud/example_dags/example_mlengine.py
+.. exampleinclude:: /../../tests/system/providers/google/cloud/ml_engine/example_mlengine.py
     :language: python
     :dedent: 4
     :start-after: [START howto_operator_gcp_mlengine_training]
@@ -55,7 +55,7 @@ A model is a container that can hold multiple model versions. A new model can be
 The ``model`` field should be defined with a dictionary containing the information about the model.
 ``name`` is a required field in this dictionary.
 
-.. exampleinclude:: /../../airflow/providers/google/cloud/example_dags/example_mlengine.py
+.. exampleinclude:: /../../tests/system/providers/google/cloud/ml_engine/example_mlengine.py
     :language: python
     :dedent: 4
     :start-after: [START howto_operator_gcp_mlengine_create_model]
@@ -69,7 +69,7 @@ The :class:`~airflow.providers.google.cloud.operators.mlengine.MLEngineGetModelO
 can be used to obtain a model previously created. To obtain the correct model, ``model_name``
 must be defined in the operator.
 
-.. exampleinclude:: /../../airflow/providers/google/cloud/example_dags/example_mlengine.py
+.. exampleinclude:: /../../tests/system/providers/google/cloud/ml_engine/example_mlengine.py
     :language: python
     :dedent: 4
     :start-after: [START howto_operator_gcp_mlengine_get_model]
@@ -80,7 +80,7 @@ fields to dynamically determine their values. The result are saved to :ref:`XCom
 allowing them to be used by other operators. In this case, the
 :class:`~airflow.operators.bash.BashOperator` is used to print the model information.
 
-.. exampleinclude:: /../../airflow/providers/google/cloud/example_dags/example_mlengine.py
+.. exampleinclude:: /../../tests/system/providers/google/cloud/ml_engine/example_mlengine.py
     :language: python
     :dedent: 4
     :start-after: [START howto_operator_gcp_mlengine_print_model]
@@ -96,7 +96,7 @@ The model must be specified by ``model_name``, and the ``version`` parameter sho
 all the information about the version. Within the ``version`` parameter's dictionary, the ``name`` field is
 required.
 
-.. exampleinclude:: /../../airflow/providers/google/cloud/example_dags/example_mlengine.py
+.. exampleinclude:: /../../tests/system/providers/google/cloud/ml_engine/example_mlengine.py
     :language: python
     :dedent: 4
     :start-after: [START howto_operator_gcp_mlengine_create_version1]
@@ -105,7 +105,7 @@ required.
 The :class:`~airflow.providers.google.cloud.operators.mlengine.MLEngineCreateVersionOperator`
 can also be used to create more versions with varying parameters.
 
-.. exampleinclude:: /../../airflow/providers/google/cloud/example_dags/example_mlengine.py
+.. exampleinclude:: /../../tests/system/providers/google/cloud/ml_engine/example_mlengine.py
     :language: python
     :dedent: 4
     :start-after: [START howto_operator_gcp_mlengine_create_version2]
@@ -120,7 +120,7 @@ By default, the model code will run using the default model version. You can set
 :class:`~airflow.providers.google.cloud.operators.mlengine.MLEngineSetDefaultVersionOperator`
 by specifying the ``model_name`` and ``version_name`` parameters.
 
-.. exampleinclude:: /../../airflow/providers/google/cloud/example_dags/example_mlengine.py
+.. exampleinclude:: /../../tests/system/providers/google/cloud/ml_engine/example_mlengine.py
     :language: python
     :dedent: 4
     :start-after: [START howto_operator_gcp_mlengine_default_version]
@@ -130,7 +130,7 @@ To list the model versions available, use the
 :class:`~airflow.providers.google.cloud.operators.mlengine.MLEngineListVersionsOperator`
 while specifying the ``model_name`` parameter.
 
-.. exampleinclude:: /../../airflow/providers/google/cloud/example_dags/example_mlengine.py
+.. exampleinclude:: /../../tests/system/providers/google/cloud/ml_engine/example_mlengine.py
     :language: python
     :dedent: 4
     :start-after: [START howto_operator_gcp_mlengine_list_versions]
@@ -141,7 +141,7 @@ fields to dynamically determine their values. The result are saved to :ref:`XCom
 allowing them to be used by other operators. In this case, the
 :class:`~airflow.operators.bash.BashOperator` is used to print the version information.
 
-.. exampleinclude:: /../../airflow/providers/google/cloud/example_dags/example_mlengine.py
+.. exampleinclude:: /../../tests/system/providers/google/cloud/ml_engine/example_mlengine.py
     :language: python
     :dedent: 4
     :start-after: [START howto_operator_gcp_mlengine_print_versions]
@@ -156,7 +156,7 @@ A Google Cloud AI Platform prediction job can be started with the
 For specifying the model origen, you need to provide either the ``model_name``, ``uri``, or ``model_name`` and
 ``version_name``. If you do not provide the ``version_name``, the operator will use the default model version.
 
-.. exampleinclude:: /../../airflow/providers/google/cloud/example_dags/example_mlengine.py
+.. exampleinclude:: /../../tests/system/providers/google/cloud/ml_engine/example_mlengine.py
     :language: python
     :dedent: 4
     :start-after: [START howto_operator_gcp_mlengine_get_prediction]
@@ -171,7 +171,7 @@ A model version can be deleted with the
 :class:`~airflow.providers.google.cloud.operators.mlengine.MLEngineDeleteVersionOperator` by
 the ``version_name`` and ``model_name`` parameters.
 
-.. exampleinclude:: /../../airflow/providers/google/cloud/example_dags/example_mlengine.py
+.. exampleinclude:: /../../tests/system/providers/google/cloud/ml_engine/example_mlengine.py
     :language: python
     :dedent: 4
     :start-after: [START howto_operator_gcp_mlengine_delete_version]
@@ -181,7 +181,7 @@ You can also delete a model with the
 :class:`~airflow.providers.google.cloud.operators.mlengine.MLEngineDeleteModelOperator`
 by providing the ``model_name`` parameter.
 
-.. exampleinclude:: /../../airflow/providers/google/cloud/example_dags/example_mlengine.py
+.. exampleinclude:: /../../tests/system/providers/google/cloud/ml_engine/example_mlengine.py
     :language: python
     :dedent: 4
     :start-after: [START howto_operator_gcp_mlengine_delete_model]
@@ -193,7 +193,7 @@ To evaluate a prediction and model, specify a metric function to generate a summ
 the evaluation of the model. This function receives a dictionary derived from a json in the batch
 prediction result, then returns a tuple of metrics.
 
-.. exampleinclude:: /../../airflow/providers/google/cloud/example_dags/example_mlengine.py
+.. exampleinclude:: /../../tests/system/providers/google/cloud/ml_engine/example_mlengine.py
     :language: python
     :dedent: 4
     :start-after: [START howto_operator_gcp_mlengine_get_metric]
@@ -203,7 +203,7 @@ To evaluate a prediction and model, it's useful to have a function to validate t
 This function receives a dictionary of the averaged metrics the function above generated. It then
 raises an exception if a task fails or should not proceed.
 
-.. exampleinclude:: /../../airflow/providers/google/cloud/example_dags/example_mlengine.py
+.. exampleinclude:: /../../tests/system/providers/google/cloud/ml_engine/example_mlengine.py
     :language: python
     :dedent: 4
     :start-after: [START howto_operator_gcp_mlengine_validate_error]
@@ -214,7 +214,7 @@ Prediction results and a model summary can be generated through a function such
 It makes predictions using the specified inputs and then summarizes and validates the result. The
 functions created above should be passed in through the ``metric_fn_and_keys`` and ``validate_fn`` fields.
 
-.. exampleinclude:: /../../airflow/providers/google/cloud/example_dags/example_mlengine.py
+.. exampleinclude:: /../../tests/system/providers/google/cloud/ml_engine/example_mlengine.py
     :language: python
     :dedent: 4
     :start-after: [START howto_operator_gcp_mlengine_evaluate]
 
@@ -0,0 +1,16 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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
+#
+#   http://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.