[CHANGE ME] Re-generated bigquery to pick up changes in the API or cl…

…ient library generator.
googleapis · Dec 3, 2019 · 4bc0a97 · 4bc0a97
1 parent 38d8f2b
commit 4bc0a97
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Showing 11 changed files with 2,374 additions and 4,028 deletions.
diff --git a/bigquery/google/cloud/bigquery_v2/gapic/enums.py b/bigquery/google/cloud/bigquery_v2/gapic/enums.py
@@ -33,14 +33,14 @@ class DataSplitMethod(enum.IntEnum):
           AUTO_SPLIT (int): Splits data automatically: Uses NO\_SPLIT if the data size is small.
           Otherwise uses RANDOM.
         """
-
         DATA_SPLIT_METHOD_UNSPECIFIED = 0
         RANDOM = 1
         CUSTOM = 2
         SEQUENTIAL = 3
         NO_SPLIT = 4
         AUTO_SPLIT = 5
 
+
     class DistanceType(enum.IntEnum):
         """
         Distance metric used to compute the distance between two points.
@@ -50,11 +50,11 @@ class DistanceType(enum.IntEnum):
           EUCLIDEAN (int): Eculidean distance.
           COSINE (int): Cosine distance.
         """
-
         DISTANCE_TYPE_UNSPECIFIED = 0
         EUCLIDEAN = 1
         COSINE = 2
 
+
     class LearnRateStrategy(enum.IntEnum):
         """
         Indicates the learning rate optimization strategy to use.
@@ -64,11 +64,11 @@ class LearnRateStrategy(enum.IntEnum):
           LINE_SEARCH (int): Use line search to determine learning rate.
           CONSTANT (int): Use a constant learning rate.
         """
-
         LEARN_RATE_STRATEGY_UNSPECIFIED = 0
         LINE_SEARCH = 1
         CONSTANT = 2
 
+
     class LossType(enum.IntEnum):
         """
         Loss metric to evaluate model training performance.
@@ -78,11 +78,11 @@ class LossType(enum.IntEnum):
           MEAN_SQUARED_LOSS (int): Mean squared loss, used for linear regression.
           MEAN_LOG_LOSS (int): Mean log loss, used for logistic regression.
         """
-
         LOSS_TYPE_UNSPECIFIED = 0
         MEAN_SQUARED_LOSS = 1
         MEAN_LOG_LOSS = 2
 
+
     class ModelType(enum.IntEnum):
         """
         Indicates the type of the Model.
@@ -94,13 +94,13 @@ class ModelType(enum.IntEnum):
           KMEANS (int): K-means clustering model.
           TENSORFLOW (int): [Beta] An imported TensorFlow model.
         """
-
         MODEL_TYPE_UNSPECIFIED = 0
         LINEAR_REGRESSION = 1
         LOGISTIC_REGRESSION = 2
         KMEANS = 3
         TENSORFLOW = 6
 
+
     class OptimizationStrategy(enum.IntEnum):
         """
         Indicates the optimization strategy used for training.
@@ -110,11 +110,11 @@ class OptimizationStrategy(enum.IntEnum):
           BATCH_GRADIENT_DESCENT (int): Uses an iterative batch gradient descent algorithm.
           NORMAL_EQUATION (int): Uses a normal equation to solve linear regression problem.
         """
-
         OPTIMIZATION_STRATEGY_UNSPECIFIED = 0
         BATCH_GRADIENT_DESCENT = 1
         NORMAL_EQUATION = 2
 
+
     class KmeansEnums(object):
         class KmeansInitializationMethod(enum.IntEnum):
             """
@@ -127,7 +127,6 @@ class KmeansInitializationMethod(enum.IntEnum):
               CUSTOM (int): Initializes the centroids using data specified in
               kmeans\_initialization\_column.
             """
-
             KMEANS_INITIALIZATION_METHOD_UNSPECIFIED = 0
             RANDOM = 1
             CUSTOM = 2
@@ -154,7 +153,6 @@ class TypeKind(enum.IntEnum):
           STRUCT (int): Encoded as a list with fields of type Type.struct\_type[i]. List is used
           because a JSON object cannot have duplicate field names.
         """
-
         TYPE_KIND_UNSPECIFIED = 0
         INT64 = 2
         BOOL = 5
@@ -169,3 +167,4 @@ class TypeKind(enum.IntEnum):
         NUMERIC = 23
         ARRAY = 16
         STRUCT = 17
+
diff --git a/bigquery/google/cloud/bigquery_v2/proto/encryption_config_pb2.py b/bigquery/google/cloud/bigquery_v2/proto/encryption_config_pb2.py
diff --git a/bigquery/google/cloud/bigquery_v2/proto/encryption_config_pb2_grpc.py b/bigquery/google/cloud/bigquery_v2/proto/encryption_config_pb2_grpc.py
@@ -1,2 +1,3 @@
 # Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
 import grpc
+