diff --git a/flink-tensorflow/pom.xml b/flink-tensorflow/pom.xml
index be2512b..a81fc90 100644
--- a/flink-tensorflow/pom.xml
+++ b/flink-tensorflow/pom.xml
@@ -101,6 +101,11 @@ under the License.
 			<artifactId>tensorflow</artifactId>
 			<version>${tf.version}</version>
 		</dependency>
+		<dependency>
+			<groupId>org.tensorflow</groupId>
+			<artifactId>proto</artifactId>
+			<version>${tf.version}</version>
+		</dependency>
 		<dependency>
 			<groupId>com.google.protobuf</groupId>
 			<artifactId>protobuf-java</artifactId>
@@ -176,23 +181,6 @@ under the License.
 				</configuration>
 			</plugin>
 
-			<plugin>
-				<groupId>org.xolstice.maven.plugins</groupId>
-				<artifactId>protobuf-maven-plugin</artifactId>
-				<version>0.5.0</version>
-				<configuration>
-					<!--<protocExecutable>/usr/local/bin/protoc</protocExecutable>-->
-				</configuration>
-				<executions>
-					<execution>
-						<goals>
-							<goal>compile</goal>
-							<goal>test-compile</goal>
-						</goals>
-					</execution>
-				</executions>
-			</plugin>
-
 			<plugin>
 				<groupId>net.alchim31.maven</groupId>
 				<artifactId>scala-maven-plugin</artifactId>
diff --git a/flink-tensorflow/src/main/proto/tensorflow/core/example/example.proto b/flink-tensorflow/src/main/proto/tensorflow/core/example/example.proto
deleted file mode 100644
index 956a374..0000000
--- a/flink-tensorflow/src/main/proto/tensorflow/core/example/example.proto
+++ /dev/null
@@ -1,294 +0,0 @@
-// Protocol messages for describing input data Examples for machine learning
-// model training or inference.
-syntax = "proto3";
-
-import "tensorflow/core/example/feature.proto";
-option cc_enable_arenas = true;
-option java_outer_classname = "ExampleProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.example";
-
-package tensorflow;
-
-// An Example is a mostly-normalized data format for storing data for
-// training and inference.  It contains a key-value store (features); where
-// each key (string) maps to a Feature message (which is oneof packed BytesList,
-// FloatList, or Int64List).  This flexible and compact format allows the
-// storage of large amounts of typed data, but requires that the data shape
-// and use be determined by the configuration files and parsers that are used to
-// read and write this format.  That is, the Example is mostly *not* a
-// self-describing format.  In TensorFlow, Examples are read in row-major
-// format, so any configuration that describes data with rank-2 or above
-// should keep this in mind.  For example, to store an M x N matrix of Bytes,
-// the BytesList must contain M*N bytes, with M rows of N contiguous values
-// each.  That is, the BytesList value must store the matrix as:
-//     .... row 0 .... .... row 1 .... // ...........  // ... row M-1 ....
-//
-// An Example for a movie recommendation application:
-//   features {
-//     feature {
-//       key: "age"
-//       value { float_list {
-//         value: 29.0
-//       }}
-//     }
-//     feature {
-//       key: "movie"
-//       value { bytes_list {
-//         value: "The Shawshank Redemption"
-//         value: "Fight Club"
-//       }}
-//     }
-//     feature {
-//       key: "movie_ratings"
-//       value { float_list {
-//         value: 9.0
-//         value: 9.7
-//       }}
-//     }
-//     feature {
-//       key: "suggestion"
-//       value { bytes_list {
-//         value: "Inception"
-//       }}
-//     }
-//     # Note that this feature exists to be used as a label in training.
-//     # E.g., if training a logistic regression model to predict purchase
-//     # probability in our learning tool we would set the label feature to
-//     # "suggestion_purchased".
-//     feature {
-//       key: "suggestion_purchased"
-//       value { float_list {
-//         value: 1.0
-//       }}
-//     }
-//     # Similar to "suggestion_purchased" above this feature exists to be used
-//     # as a label in training.
-//     # E.g., if training a linear regression model to predict purchase
-//     # price in our learning tool we would set the label feature to
-//     # "purchase_price".
-//     feature {
-//       key: "purchase_price"
-//       value { float_list {
-//         value: 9.99
-//       }}
-//     }
-//  }
-//
-// A conformant Example data set obeys the following conventions:
-//   - If a Feature K exists in one example with data type T, it must be of
-//       type T in all other examples when present. It may be omitted.
-//   - The number of instances of Feature K list data may vary across examples,
-//       depending on the requirements of the model.
-//   - If a Feature K doesn't exist in an example, a K-specific default will be
-//       used, if configured.
-//   - If a Feature K exists in an example but contains no items, the intent
-//       is considered to be an empty tensor and no default will be used.
-
-message Example {
-  Features features = 1;
-};
-
-// A SequenceExample is an Example representing one or more sequences, and
-// some context.  The context contains features which apply to the entire
-// example. The feature_lists contain a key, value map where each key is
-// associated with a repeated set of Features (a FeatureList).
-// A FeatureList thus represents the values of a feature identified by its key
-// over time / frames.
-//
-// Below is a SequenceExample for a movie recommendation application recording a
-// sequence of ratings by a user. The time-independent features ("locale",
-// "age", "favorites") describing the user are part of the context. The sequence
-// of movies the user rated are part of the feature_lists. For each movie in the
-// sequence we have information on its name and actors and the user's rating.
-// This information is recorded in three separate feature_list(s).
-// In the example below there are only two movies. All three feature_list(s),
-// namely "movie_ratings", "movie_names", and "actors" have a feature value for
-// both movies. Note, that "actors" is itself a bytes_list with multiple
-// strings per movie.
-//
-// context: {
-//   feature: {
-//     key  : "locale"
-//     value: {
-//       bytes_list: {
-//         value: [ "pt_BR" ]
-//       }
-//     }
-//   }
-//   feature: {
-//     key  : "age"
-//     value: {
-//       float_list: {
-//         value: [ 19.0 ]
-//       }
-//     }
-//   }
-//   feature: {
-//     key  : "favorites"
-//     value: {
-//       bytes_list: {
-//         value: [ "Majesty Rose", "Savannah Outen", "One Direction" ]
-//       }
-//     }
-//   }
-// }
-// feature_lists: {
-//   feature_list: {
-//     key  : "movie_ratings"
-//     value: {
-//       feature: {
-//         float_list: {
-//           value: [ 4.5 ]
-//         }
-//       }
-//       feature: {
-//         float_list: {
-//           value: [ 5.0 ]
-//         }
-//       }
-//     }
-//   }
-//   feature_list: {
-//     key  : "movie_names"
-//     value: {
-//       feature: {
-//         bytes_list: {
-//           value: [ "The Shawshank Redemption" ]
-//         }
-//       }
-//       feature: {
-//         bytes_list: {
-//           value: [ "Fight Club" ]
-//         }
-//       }
-//     }
-//   }
-//   feature_list: {
-//     key  : "actors"
-//     value: {
-//       feature: {
-//         bytes_list: {
-//           value: [ "Tim Robbins", "Morgan Freeman" ]
-//         }
-//       }
-//       feature: {
-//         bytes_list: {
-//           value: [ "Brad Pitt", "Edward Norton", "Helena Bonham Carter" ]
-//         }
-//       }
-//     }
-//   }
-// }
-//
-// A conformant SequenceExample data set obeys the following conventions:
-//
-// Context:
-//   - All conformant context features K must obey the same conventions as
-//     a conformant Example's features (see above).
-// Feature lists:
-//   - A FeatureList L may be missing in an example; it is up to the
-//     parser configuration to determine if this is allowed or considered
-//     an empty list (zero length).
-//   - If a FeatureList L exists, it may be empty (zero length).
-//   - If a FeatureList L is non-empty, all features within the FeatureList
-//     must have the same data type T. Even across SequenceExamples, the type T
-//     of the FeatureList identified by the same key must be the same.
-//   - If a FeatureList L is non-empty, it is up to the parser configuration
-//     to determine if all features within the FeatureList must
-//     have the same size.  The same holds for this FeatureList across multiple
-//     examples.
-//
-// Examples of conformant and non-conformant examples' FeatureLists:
-//
-// Conformant FeatureLists:
-//    feature_lists: { feature_list: {
-//      key: "movie_ratings"
-//      value: { feature: { float_list: { value: [ 4.5 ] } }
-//               feature: { float_list: { value: [ 5.0 ] } } }
-//    } }
-//
-// Non-conformant FeatureLists (mismatched types):
-//    feature_lists: { feature_list: {
-//      key: "movie_ratings"
-//      value: { feature: { float_list: { value: [ 4.5 ] } }
-//               feature: { int64_list: { value: [ 5 ] } } }
-//    } }
-//
-// Conditionally conformant FeatureLists, the parser configuration determines
-// if the feature sizes must match:
-//    feature_lists: { feature_list: {
-//      key: "movie_ratings"
-//      value: { feature: { float_list: { value: [ 4.5 ] } }
-//               feature: { float_list: { value: [ 5.0, 6.0 ] } } }
-//    } }
-//
-// Conformant pair of SequenceExample
-//    feature_lists: { feature_list: {
-//      key: "movie_ratings"
-//      value: { feature: { float_list: { value: [ 4.5 ] } }
-//               feature: { float_list: { value: [ 5.0 ] } } }
-//    } }
-// and:
-//    feature_lists: { feature_list: {
-//      key: "movie_ratings"
-//      value: { feature: { float_list: { value: [ 4.5 ] } }
-//               feature: { float_list: { value: [ 5.0 ] } }
-//               feature: { float_list: { value: [ 2.0 ] } } }
-//    } }
-//
-// Conformant pair of SequenceExample
-//    feature_lists: { feature_list: {
-//      key: "movie_ratings"
-//      value: { feature: { float_list: { value: [ 4.5 ] } }
-//               feature: { float_list: { value: [ 5.0 ] } } }
-//    } }
-// and:
-//    feature_lists: { feature_list: {
-//      key: "movie_ratings"
-//      value: { }
-//    } }
-//
-// Conditionally conformant pair of SequenceExample, the parser configuration
-// determines if the second feature_lists is consistent (zero-length) or
-// invalid (missing "movie_ratings"):
-//    feature_lists: { feature_list: {
-//      key: "movie_ratings"
-//      value: { feature: { float_list: { value: [ 4.5 ] } }
-//               feature: { float_list: { value: [ 5.0 ] } } }
-//    } }
-// and:
-//    feature_lists: { }
-//
-// Non-conformant pair of SequenceExample (mismatched types)
-//    feature_lists: { feature_list: {
-//      key: "movie_ratings"
-//      value: { feature: { float_list: { value: [ 4.5 ] } }
-//               feature: { float_list: { value: [ 5.0 ] } } }
-//    } }
-// and:
-//    feature_lists: { feature_list: {
-//      key: "movie_ratings"
-//      value: { feature: { int64_list: { value: [ 4 ] } }
-//               feature: { int64_list: { value: [ 5 ] } }
-//               feature: { int64_list: { value: [ 2 ] } } }
-//    } }
-//
-// Conditionally conformant pair of SequenceExample; the parser configuration
-// determines if the feature sizes must match:
-//    feature_lists: { feature_list: {
-//      key: "movie_ratings"
-//      value: { feature: { float_list: { value: [ 4.5 ] } }
-//               feature: { float_list: { value: [ 5.0 ] } } }
-//    } }
-// and:
-//    feature_lists: { feature_list: {
-//      key: "movie_ratings"
-//      value: { feature: { float_list: { value: [ 4.0 ] } }
-//               feature: { float_list: { value: [ 5.0, 3.0 ] } }
-//    } }
-
-message SequenceExample {
-  Features context = 1;
-  FeatureLists feature_lists = 2;
-};
diff --git a/flink-tensorflow/src/main/proto/tensorflow/core/example/feature.proto b/flink-tensorflow/src/main/proto/tensorflow/core/example/feature.proto
deleted file mode 100644
index da3dc59..0000000
--- a/flink-tensorflow/src/main/proto/tensorflow/core/example/feature.proto
+++ /dev/null
@@ -1,105 +0,0 @@
-// Protocol messages for describing features for machine learning model
-// training or inference.
-//
-// There are three base Feature types:
-//   - bytes
-//   - float
-//   - int64
-//
-// A Feature contains Lists which may hold zero or more values.  These
-// lists are the base values BytesList, FloatList, Int64List.
-//
-// Features are organized into categories by name.  The Features message
-// contains the mapping from name to Feature.
-//
-// Example Features for a movie recommendation application:
-//   feature {
-//     key: "age"
-//     value { float_list {
-//       value: 29.0
-//     }}
-//   }
-//   feature {
-//     key: "movie"
-//     value { bytes_list {
-//       value: "The Shawshank Redemption"
-//       value: "Fight Club"
-//     }}
-//   }
-//   feature {
-//     key: "movie_ratings"
-//     value { float_list {
-//       value: 9.0
-//       value: 9.7
-//     }}
-//   }
-//   feature {
-//     key: "suggestion"
-//     value { bytes_list {
-//       value: "Inception"
-//     }}
-//   }
-//   feature {
-//     key: "suggestion_purchased"
-//     value { int64_list {
-//       value: 1
-//     }}
-//   }
-//   feature {
-//     key: "purchase_price"
-//     value { float_list {
-//       value: 9.99
-//     }}
-//   }
-//
-
-syntax = "proto3";
-option cc_enable_arenas = true;
-option java_outer_classname = "FeatureProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.example";
-
-package tensorflow;
-
-// Containers to hold repeated fundamental values.
-message BytesList {
-  repeated bytes value = 1;
-}
-message FloatList {
-  repeated float value = 1 [packed = true];
-}
-message Int64List {
-  repeated int64 value = 1 [packed = true];
-}
-
-// Containers for non-sequential data.
-message Feature {
-  // Each feature can be exactly one kind.
-  oneof kind {
-    BytesList bytes_list = 1;
-    FloatList float_list = 2;
-    Int64List int64_list = 3;
-  }
-};
-
-message Features {
-  // Map from feature name to feature.
-  map<string, Feature> feature = 1;
-};
-
-// Containers for sequential data.
-//
-// A FeatureList contains lists of Features.  These may hold zero or more
-// Feature values.
-//
-// FeatureLists are organized into categories by name.  The FeatureLists message
-// contains the mapping from name to FeatureList.
-//
-message FeatureList {
-  repeated Feature feature = 1;
-};
-
-message FeatureLists {
-  // Map from feature name to feature list.
-  map<string, FeatureList> feature_list = 1;
-};
diff --git a/flink-tensorflow/src/main/proto/tensorflow/core/framework/allocation_description.proto b/flink-tensorflow/src/main/proto/tensorflow/core/framework/allocation_description.proto
deleted file mode 100644
index bb1037c..0000000
--- a/flink-tensorflow/src/main/proto/tensorflow/core/framework/allocation_description.proto
+++ /dev/null
@@ -1,27 +0,0 @@
-syntax = "proto3";
-
-package tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "AllocationDescriptionProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-message AllocationDescription {
-  // Total number of bytes requested
-  int64 requested_bytes = 1;
-
-  // Total number of bytes allocated if known
-  int64 allocated_bytes = 2;
-
-  // Name of the allocator used
-  string allocator_name = 3;
-
-  // Identifier of the allocated buffer if known
-  int64 allocation_id = 4;
-
-  // Set if this tensor only has one remaining reference
-  bool has_single_reference = 5;
-
-  // Address of the allocation.
-  uint64 ptr = 6;
-};
diff --git a/flink-tensorflow/src/main/proto/tensorflow/core/framework/attr_value.proto b/flink-tensorflow/src/main/proto/tensorflow/core/framework/attr_value.proto
deleted file mode 100644
index f115329..0000000
--- a/flink-tensorflow/src/main/proto/tensorflow/core/framework/attr_value.proto
+++ /dev/null
@@ -1,62 +0,0 @@
-syntax = "proto3";
-
-package tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "AttrValueProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "tensorflow/core/framework/tensor.proto";
-import "tensorflow/core/framework/tensor_shape.proto";
-import "tensorflow/core/framework/types.proto";
-
-// Protocol buffer representing the value for an attr used to configure an Op.
-// Comment indicates the corresponding attr type.  Only the field matching the
-// attr type may be filled.
-message AttrValue {
-  // LINT.IfChange
-  message ListValue {
-    repeated bytes s = 2;                        // "list(string)"
-    repeated int64 i = 3 [packed = true];        // "list(int)"
-    repeated float f = 4 [packed = true];        // "list(float)"
-    repeated bool b = 5 [packed = true];         // "list(bool)"
-    repeated DataType type = 6 [packed = true];  // "list(type)"
-    repeated TensorShapeProto shape = 7;         // "list(shape)"
-    repeated TensorProto tensor = 8;             // "list(tensor)"
-    // TODO(zhifengc/josh11b): implements list(func) if needed.
-  }
-  // LINT.ThenChange(https://www.tensorflow.org/code/tensorflow/c/c_api.cc)
-
-  oneof value {
-    bytes s = 2;                 // "string"
-    int64 i = 3;                 // "int"
-    float f = 4;                 // "float"
-    bool b = 5;                  // "bool"
-    DataType type = 6;           // "type"
-    TensorShapeProto shape = 7;  // "shape"
-    TensorProto tensor = 8;      // "tensor"
-    ListValue list = 1;          // any "list(...)"
-
-    // "func" represents a function. func.name is a function's name or
-    // a primitive op's name. func.attr.first is the name of an attr
-    // defined for that function. func.attr.second is the value for
-    // that attr in the instantiation.
-    NameAttrList func = 10;
-
-    // This is a placeholder only used in nodes defined inside a
-    // function.  It indicates the attr value will be supplied when
-    // the function is instantiated.  For example, let us suppose a
-    // node "N" in function "FN". "N" has an attr "A" with value
-    // placeholder = "foo". When FN is instantiated with attr "foo"
-    // set to "bar", the instantiated node N's attr A will have been
-    // given the value "bar".
-    string placeholder = 9;
-  }
-}
-
-// A list of attr names and their values. The whole list is attached
-// with a string name.  E.g., MatMul[T=float].
-message NameAttrList {
-  string name = 1;
-  map<string, AttrValue> attr = 2;
-}
diff --git a/flink-tensorflow/src/main/proto/tensorflow/core/framework/cost_graph.proto b/flink-tensorflow/src/main/proto/tensorflow/core/framework/cost_graph.proto
deleted file mode 100644
index 8145486..0000000
--- a/flink-tensorflow/src/main/proto/tensorflow/core/framework/cost_graph.proto
+++ /dev/null
@@ -1,59 +0,0 @@
-syntax = "proto3";
-
-package tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "CostGraphProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "tensorflow/core/framework/tensor_shape.proto";
-import "tensorflow/core/framework/types.proto";
-
-message CostGraphDef {
-  message Node {
-    // The name of the node. Names are globally unique.
-    string name = 1;
-
-    // The device of the node. Can be empty if the node is mapped to the
-    // default partition or partitioning hasn't been run yet.
-    string device = 2;
-
-    // The id of the node. Node ids are only unique inside a partition.
-    int32 id = 3;
-
-    // Inputs of this node. They must be executed before this node can be
-    // executed. An input is a particular output of another node, specified
-    // by the node id and the output index.
-    message InputInfo {
-      int32 preceding_node = 1;
-      int32 preceding_port = 2;
-    }
-    repeated InputInfo input_info = 4;
-
-    // Outputs of this node.
-    message OutputInfo {
-      int64 size = 1;
-      // If >= 0, the output is an alias of an input. Note that an alias input
-      // may itself be an alias. The algorithm will therefore need to follow
-      // those pointers.
-      int64 alias_input_port = 2;
-      TensorShapeProto shape = 3;
-      DataType dtype = 4;
-    }
-    repeated OutputInfo output_info = 5;
-
-    // Temporary memory used by this node.
-    int64 temporary_memory_size = 6;
-
-    // Estimate of the computational cost of this node.
-    int64 compute_cost = 9;
-
-    // If true, the output is permanent: it can't be discarded, because this
-    // node is part of the "final output". Nodes may depend on final nodes.
-    bool is_final = 7;
-
-    // Ids of the control inputs for this node.
-    repeated int32 control_input = 8;
-  }
-  repeated Node node = 1;
-}
diff --git a/flink-tensorflow/src/main/proto/tensorflow/core/framework/device_attributes.proto b/flink-tensorflow/src/main/proto/tensorflow/core/framework/device_attributes.proto
deleted file mode 100644
index 9983bcb..0000000
--- a/flink-tensorflow/src/main/proto/tensorflow/core/framework/device_attributes.proto
+++ /dev/null
@@ -1,35 +0,0 @@
-syntax = "proto3";
-
-package tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "DeviceAttributesProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-message DeviceLocality {
-  // Optional bus locality of device.  Default value of 0 means
-  // no specific locality.  Specific localities are indexed from 1.
-  int32 bus_id = 1;
-};
-
-message DeviceAttributes {
-  // Fully specified name of the device within a cluster.
-  string name = 1;
-
-  // String representation of device_type.
-  string device_type = 2;
-
-  // Memory capacity of device in bytes.
-  int64 memory_limit = 4;
-
-  // Platform-specific data about device that may be useful
-  // for supporting efficient data transfers.
-  DeviceLocality locality = 5;
-
-  // A device is assigned a global unique number each time it is
-  // initialized. "incarnation" should never be 0.
-  fixed64 incarnation = 6;
-
-  // String representation of the physical device that this device maps to.
-  string physical_device_desc = 7;
-}
diff --git a/flink-tensorflow/src/main/proto/tensorflow/core/framework/function.proto b/flink-tensorflow/src/main/proto/tensorflow/core/framework/function.proto
deleted file mode 100644
index 5a394d6..0000000
--- a/flink-tensorflow/src/main/proto/tensorflow/core/framework/function.proto
+++ /dev/null
@@ -1,151 +0,0 @@
-syntax = "proto3";
-
-package tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "FunctionProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "tensorflow/core/framework/attr_value.proto";
-import "tensorflow/core/framework/node_def.proto";
-import "tensorflow/core/framework/op_def.proto";
-
-// A library is a set of named functions.
-message FunctionDefLibrary {
-  repeated FunctionDef function = 1;
-  repeated GradientDef gradient = 2;
-}
-
-// A function can be instantiated when the runtime can bind every attr
-// with a value. When a GraphDef has a call to a function, it must
-// have binding for every attr defined in the signature.
-//
-// TODO(zhifengc):
-//   * device spec, etc.
-message FunctionDef {
-  // The definition of the function's name, arguments, return values,
-  // attrs etc.
-  OpDef signature = 1;
-
-  // Attributes specific to this function definition.
-  map<string, AttrValue> attr = 5;
-
-  // TO BE REPLACED
-
-  // The body of the function.
-  repeated Node node = 2;  // function.node.ret[*] are unique.
-
-  // A node is a multi-value assignment:
-  //   (ret[0], ret[1], ...) = func(arg[0], arg[1], ...)
-  //
-  // By convention, "func" is resolved by consulting with a user-defined
-  // library first. If not resolved, "func" is assumed to be a builtin op.
-  message Node {
-    // This node produces multiple outputs. They are named ret[0],
-    // ret[1], ..., etc.
-    //
-    // REQUIRES: function.node.ret[*] are unique across all nodes.
-    // REQUIRES: ret.size == func/op def's number of output args.
-    repeated string ret = 1;
-
-    // The op/function name.
-    string op = 2;
-
-    // Arguments passed to this func/op.
-    //
-    // arg[i] must be either one of
-    // function.signature.input_args[*].name or one of
-    // function.node[*].ret[*].
-    //
-    // REQUIRES: arg.size == func/op def's number of input args.
-    repeated string arg = 3;
-
-    // Control dependencies.
-    //
-    // dep[i] must be one of function.node[*].ret[*] or one of
-    // function.signature.input_args[*].name.
-    repeated string dep = 4;
-
-    // Attrs.
-    //
-    // 'attr' maps names defined by 'func's attr defs to attr values.
-    // attr values may have placeholders which are substituted
-    // recursively by concrete values when this node is instantiated.
-    // These placeholders must name an attr listed in the FunctionDef's
-    // signature.
-    map<string, AttrValue> attr = 5;
-  }
-
-  // WILL REPLACE THE ABOVE
-
-  // If node_def is present, and the consumer is at GraphDef version
-  // >= 12, then these fields are used and `node` is ignored.  If the
-  // consumer's GraphDef version is < 12 or this field is empty, then
-  // `node` is used.  This allows producers to fill both fields to
-  // remain compatible with old consumers.  At some future GraphDef
-  // version, `node` will be ignored even if `node_def` is empty.
-  // TODO(josh11b): Finish this transition.
-
-  // In both of the following fields, there is the need to specify an
-  // output that is used as either the input to another node (in
-  // `node_def`) or as a return value of the function (in `ret`).
-  // Unlike the NodeDefs in GraphDef, we need to be able to specify a
-  // list in some cases (instead of just single outputs).  Also, we
-  // need to be able to deal with lists of unknown length (so the
-  // output index may not be known at function definition time).  So
-  // we use the following format instead:
-  // * "fun_in" where "fun_in" is the name of a function input arg in
-  //   the `signature` field above.  This represents that input, whether
-  //   it is a single tensor or a list.
-  // * "fun_in:0" gives the first element of a function input arg (a
-  //   non-list input is considered a list of length 1 for these
-  //   purposes).
-  // * "node:out" where "node" is the name of a node in `node_def` and
-  //   "out" is the name one of its op's output arguments (the name
-  //   comes from the OpDef of the node's op). This represents that
-  //   node's output, whether it is a single tensor or a list.
-  //   Note: We enforce that an op's output arguments are never
-  //   renamed in the backwards-compatibility test.
-  // * "node:out:0" gives the first element of a node output arg (a
-  //   non-list output is considered a list of length 1 for these
-  //   purposes).
-  //
-  // NOT CURRENTLY SUPPORTED (but may be in the future):
-  // * "node:out:-1" gives last element in a node output list
-  // * "node:out:1:" gives a list with all but the first element in a
-  //   node output list
-  // * "node:out::-1" gives a list with all but the last element in a
-  //   node output list
-
-  // The body of the function.  Unlike the NodeDefs in a GraphDef, attrs
-  // may have values of type `placeholder` and the `input` field uses
-  // the "output" format above.
-  repeated NodeDef node_def = 3;
-
-  // A mapping from the output arg names from `signature` to the
-  // outputs from `node_def` that should be returned by the function.
-  map<string, string> ret = 4;
-}
-
-// GradientDef defines the gradient function of a function defined in
-// a function library.
-//
-// A gradient function g (specified by gradient_func) for a function f
-// (specified by function_name) must follow the following:
-//
-// The function 'f' must be a numerical function which takes N inputs
-// and produces M outputs. Its gradient function 'g', which is a
-// function taking N + M inputs and produces N outputs.
-//
-// I.e. if we have
-//    (y1, y2, ..., y_M) = f(x1, x2, ..., x_N),
-// then, g is
-//    (dL/dx1, dL/dx2, ..., dL/dx_N) = g(x1, x2, ..., x_N,
-//                                      dL/dy1, dL/dy2, ..., dL/dy_M),
-// where L is a scalar-value function of (x1, x2, ..., xN) (e.g., the
-// loss function). dL/dx_i is the partial derivative of L with respect
-// to x_i.
-message GradientDef {
-  string function_name = 1;  // The function name.
-  string gradient_func = 2;  // The gradient function's name.
-}
diff --git a/flink-tensorflow/src/main/proto/tensorflow/core/framework/graph.proto b/flink-tensorflow/src/main/proto/tensorflow/core/framework/graph.proto
deleted file mode 100644
index 7d6e16d..0000000
--- a/flink-tensorflow/src/main/proto/tensorflow/core/framework/graph.proto
+++ /dev/null
@@ -1,56 +0,0 @@
-syntax = "proto3";
-
-package tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "GraphProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "tensorflow/core/framework/node_def.proto";
-import "tensorflow/core/framework/function.proto";
-import "tensorflow/core/framework/versions.proto";
-
-// Represents the graph of operations
-message GraphDef {
-  repeated NodeDef node = 1;
-
-  // Compatibility versions of the graph.  See core/public/version.h for version
-  // history.  The GraphDef version is distinct from the TensorFlow version, and
-  // each release of TensorFlow will support a range of GraphDef versions.
-  VersionDef versions = 4;
-
-  // Deprecated single version field; use versions above instead.  Since all
-  // GraphDef changes before "versions" was introduced were forward
-  // compatible, this field is entirely ignored.
-  int32 version = 3 [deprecated = true];
-
-  // EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
-  //
-  // "library" provides user-defined functions.
-  //
-  // Naming:
-  //   * library.function.name are in a flat namespace.
-  //     NOTE: We may need to change it to be hierarchical to support
-  //     different orgs. E.g.,
-  //     { "/google/nn", { ... }},
-  //     { "/google/vision", { ... }}
-  //     { "/org_foo/module_bar", { ... }}
-  //     map<string, FunctionDefLib> named_lib;
-  //   * If node[i].op is the name of one function in "library",
-  //     node[i] is deemed as a function call. Otherwise, node[i].op
-  //     must be a primitive operation supported by the runtime.
-  //
-  //
-  // Function call semantics:
-  //
-  //   * The callee may start execution as soon as some of its inputs
-  //     are ready. The caller may want to use Tuple() mechanism to
-  //     ensure all inputs are ready in the same time.
-  //
-  //   * The consumer of return values may start executing as soon as
-  //     the return values the consumer depends on are ready.  The
-  //     consumer may want to use Tuple() mechanism to ensure the
-  //     consumer does not start until all return values of the callee
-  //     function are ready.
-  FunctionDefLibrary library = 2;
-};
diff --git a/flink-tensorflow/src/main/proto/tensorflow/core/framework/kernel_def.proto b/flink-tensorflow/src/main/proto/tensorflow/core/framework/kernel_def.proto
deleted file mode 100644
index 65e9ef0..0000000
--- a/flink-tensorflow/src/main/proto/tensorflow/core/framework/kernel_def.proto
+++ /dev/null
@@ -1,36 +0,0 @@
-syntax = "proto3";
-
-package tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "KernelDefProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "tensorflow/core/framework/attr_value.proto";
-
-message KernelDef {
-  // Must match the name of an Op.
-  string op = 1;
-
-  // Type of device this kernel runs on.
-  string device_type = 2;
-
-  message AttrConstraint {
-    // Name of an attr from the Op.
-    string name = 1;
-
-    // A list of values that this kernel supports for this attr.
-    // Like OpDef.AttrDef.allowed_values, except for kernels instead of Ops.
-    AttrValue allowed_values = 2;
-  }
-  repeated AttrConstraint constraint = 3;
-
-  // Names of the Op's input_/output_args that reside in host memory
-  // instead of device memory.
-  repeated string host_memory_arg = 4;
-
-  // This allows experimental kernels to be registered for an op that
-  // won't be used unless the user specifies a "_kernel" attr with
-  // value matching this.
-  string label = 5;
-}
diff --git a/flink-tensorflow/src/main/proto/tensorflow/core/framework/log_memory.proto b/flink-tensorflow/src/main/proto/tensorflow/core/framework/log_memory.proto
deleted file mode 100644
index d1e1263..0000000
--- a/flink-tensorflow/src/main/proto/tensorflow/core/framework/log_memory.proto
+++ /dev/null
@@ -1,93 +0,0 @@
-syntax = "proto3";
-
-package tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "LogMemoryProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "tensorflow/core/framework/tensor_description.proto";
-
-message MemoryLogStep {
-  // Process-unique step id.
-  int64 step_id = 1;
-
-  // Handle describing the feeds and fetches of the step.
-  string handle = 2;
-};
-
-message MemoryLogTensorAllocation {
-  // Process-unique step id.
-  int64 step_id = 1;
-
-  // Name of the kernel making the allocation as set in GraphDef,
-  // e.g., "affine2/weights/Assign".
-  string kernel_name = 2;
-
-  // Allocated tensor details.
-  TensorDescription tensor = 3;
-};
-
-message MemoryLogTensorDeallocation {
-  // Id of the tensor buffer being deallocated, used to match to a
-  // corresponding allocation.
-  int64 allocation_id = 1;
-
-  // Name of the allocator used.
-  string allocator_name = 2;
-};
-
-message MemoryLogTensorOutput {
-  // Process-unique step id.
-  int64 step_id = 1;
-
-  // Name of the kernel producing an output as set in GraphDef, e.g.,
-  // "affine2/weights/Assign".
-  string kernel_name = 2;
-
-  // Index of the output being set.
-  int32 index = 3;
-
-  // Output tensor details.
-  TensorDescription tensor = 4;
-}
-
-message MemoryLogRawAllocation {
-  // Process-unique step id.
-  int64 step_id = 1;
-
-  // Name of the operation making the allocation.
-  string operation = 2;
-
-  // Number of bytes in the allocation.
-  int64 num_bytes = 3;
-
-  // Address of the allocation.
-  uint64 ptr = 4;
-
-  // Id of the tensor buffer being allocated, used to match to a
-  // corresponding deallocation.
-  int64 allocation_id = 5;
-
-  // Name of the allocator used.
-  string allocator_name = 6;
-};
-
-message MemoryLogRawDeallocation {
-  // Process-unique step id.
-  int64 step_id = 1;
-
-  // Name of the operation making the deallocation.
-  string operation = 2;
-
-  // Id of the tensor buffer being deallocated, used to match to a
-  // corresponding allocation.
-  int64 allocation_id = 3;
-
-  // Name of the allocator used.
-  string allocator_name = 4;
-
-  // True if the deallocation is queued and will be performed later,
-  // e.g. for GPU lazy freeing of buffers.
-  bool deferred = 5;
-};
diff --git a/flink-tensorflow/src/main/proto/tensorflow/core/framework/node_def.proto b/flink-tensorflow/src/main/proto/tensorflow/core/framework/node_def.proto
deleted file mode 100644
index 8d38115..0000000
--- a/flink-tensorflow/src/main/proto/tensorflow/core/framework/node_def.proto
+++ /dev/null
@@ -1,65 +0,0 @@
-syntax = "proto3";
-
-package tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "NodeProto";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "tensorflow/core/framework/attr_value.proto";
-
-message NodeDef {
-  // The name given to this operator. Used for naming inputs,
-  // logging, visualization, etc.  Unique within a single GraphDef.
-  // Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_./]*".
-  string name = 1;
-
-  // The operation name.  There may be custom parameters in attrs.
-  // Op names starting with an underscore are reserved for internal use.
-  string op = 2;
-
-  // Each input is "node:src_output" with "node" being a string name and
-  // "src_output" indicating which output tensor to use from "node". If
-  // "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
-  // may optionally be followed by control inputs that have the format
-  // "^node".
-  repeated string input = 3;
-
-  // A (possibly partial) specification for the device on which this
-  // node should be placed.
-  // The expected syntax for this string is as follows:
-  //
-  // DEVICE_SPEC ::= COLOCATED_NODE | PARTIAL_SPEC
-  //
-  // COLOCATED_NODE ::= "@" NODE_NAME  // See NodeDef.name above.
-  // PARTIAL_SPEC ::= ("/" CONSTRAINT) *
-  // CONSTRAINT ::= ("job:" JOB_NAME)
-  //              | ("replica:" [1-9][0-9]*)
-  //              | ("task:" [1-9][0-9]*)
-  //              | ( ("gpu" | "cpu") ":" ([1-9][0-9]* | "*") )
-  //
-  // Valid values for this string include:
-  // * "@other/node"                         (colocate with "other/node")
-  // * "/job:worker/replica:0/task:1/gpu:3"  (full specification)
-  // * "/job:worker/gpu:3"                   (partial specification)
-  // * ""                                    (no specification)
-  //
-  // If the constraints do not resolve to a single device (or if this
-  // field is empty or not present), the runtime will attempt to
-  // choose a device automatically.
-  string device = 4;
-
-  // Operation-specific graph-construction-time configuration.
-  // Note that this should include all attrs defined in the
-  // corresponding OpDef, including those with a value matching
-  // the default -- this allows the default to change and makes
-  // NodeDefs easier to interpret on their own.  However, if
-  // an attr with a default is not specified in this list, the
-  // default will be used.
-  // The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
-  // one of the names from the corresponding OpDef's attr field).
-  // The values must have a type matching the corresponding OpDef
-  // attr's type field.
-  // TODO(josh11b): Add some examples here showing best practices.
-  map<string, AttrValue> attr = 5;
-};
diff --git a/flink-tensorflow/src/main/proto/tensorflow/core/framework/op_def.proto b/flink-tensorflow/src/main/proto/tensorflow/core/framework/op_def.proto
deleted file mode 100644
index acb480e..0000000
--- a/flink-tensorflow/src/main/proto/tensorflow/core/framework/op_def.proto
+++ /dev/null
@@ -1,157 +0,0 @@
-syntax = "proto3";
-
-package tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "OpDefProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "tensorflow/core/framework/attr_value.proto";
-import "tensorflow/core/framework/types.proto";
-
-// Defines an operation. A NodeDef in a GraphDef specifies an Op by
-// using the "op" field which should match the name of a OpDef.
-message OpDef {
-  // Op names starting with an underscore are reserved for internal use.
-  // Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9_]*".
-  string name = 1;
-
-  // For describing inputs and outputs.
-  message ArgDef {
-    // Name for the input/output.  Should match the regexp "[a-z][a-z0-9_]*".
-    string name = 1;
-
-    // Human readable description.
-    string description = 2;
-
-    // Describes the type of one or more tensors that are accepted/produced
-    // by this input/output arg.  The only legal combinations are:
-    // * For a single tensor: either the "type" field is set or the
-    //   "type_attr" field is set to the name of an attr with type "type".
-    // * For a sequence of tensors with the same type: the "number_attr"
-    //   field will be set to the name of an attr with type "int", and
-    //   either the "type" or "type_attr" field will be set as for
-    //   single tensors.
-    // * For a sequence of tensors, the "type_list_attr" field will be set
-    //   to the name of an attr with type "list(type)".
-    DataType type = 3;
-    string type_attr = 4;    // if specified, attr must have type "type"
-    string number_attr = 5;  // if specified, attr must have type "int"
-    // If specified, attr must have type "list(type)", and none of
-    // type, type_attr, and number_attr may be specified.
-    string type_list_attr = 6;
-
-    // For inputs: if true, the inputs are required to be refs.
-    //   By default, inputs can be either refs or non-refs.
-    // For outputs: if true, outputs are refs, otherwise they are not.
-    bool is_ref = 16;
-  };
-
-  // Description of the input(s).
-  repeated ArgDef input_arg = 2;
-
-  // Description of the output(s).
-  repeated ArgDef output_arg = 3;
-
-  // Description of the graph-construction-time configuration of this
-  // Op.  That is to say, this describes the attr fields that will
-  // be specified in the NodeDef.
-  message AttrDef {
-    // A descriptive name for the argument.  May be used, e.g. by the
-    // Python client, as a keyword argument name, and so should match
-    // the regexp "[a-z][a-z0-9_]+".
-    string name = 1;
-
-    // One of the type names from attr_value.proto ("string", "list(string)",
-    // "int", etc.).
-    string type = 2;
-
-    // A reasonable default for this attribute if the user does not supply
-    // a value.  If not specified, the user must supply a value.
-    AttrValue default_value = 3;
-
-    // Human-readable description.
-    string description = 4;
-
-    // TODO(josh11b): bool is_optional?
-
-    // --- Constraints ---
-    // These constraints are only in effect if specified.  Default is no
-    // constraints.
-
-    // For type == "int", this is a minimum value.  For "list(___)"
-    // types, this is the minimum length.
-    bool has_minimum = 5;
-    int64 minimum = 6;
-
-    // The set of allowed values.  Has type that is the "list" version
-    // of the "type" field above (uses the "list" field of AttrValue).
-    // If type == "type" or "list(type)" above, then the "type" field
-    // of "allowed_values.list" has the set of allowed DataTypes.
-    // If type == "string" or "list(string)", then the "s" field of
-    // "allowed_values.list" has the set of allowed strings.
-    AttrValue allowed_values = 7;
-  }
-  repeated AttrDef attr = 4;
-
-  // Optional deprecation based on GraphDef versions.
-  OpDeprecation deprecation = 8;
-
-  // One-line human-readable description of what the Op does.
-  string summary = 5;
-
-  // Additional, longer human-readable description of what the Op does.
-  string description = 6;
-
-  // -------------------------------------------------------------------------
-  // Which optimizations this operation can participate in.
-
-  // True if the operation is commutative ("op(a,b) == op(b,a)" for all inputs)
-  bool is_commutative = 18;
-
-  // If is_aggregate is true, then this operation accepts N >= 2
-  // inputs and produces 1 output all of the same type.  Should be
-  // associative and commutative, and produce output with the same
-  // shape as the input.  The optimizer may replace an aggregate op
-  // taking input from multiple devices with a tree of aggregate ops
-  // that aggregate locally within each device (and possibly within
-  // groups of nearby devices) before communicating.
-  // TODO(josh11b): Implement that optimization.
-  bool is_aggregate = 16;  // for things like add
-
-  // Other optimizations go here, like
-  //   can_alias_input, rewrite_when_output_unused, partitioning_strategy, etc.
-
-  // -------------------------------------------------------------------------
-  // Optimization constraints.
-
-  // By default Ops may be moved between devices.  Stateful ops should
-  // either not be moved, or should only be moved if that state can also
-  // be moved (e.g. via some sort of save / restore).
-  // Stateful ops are guaranteed to never be optimized away by Common
-  // Subexpression Elimination (CSE).
-  bool is_stateful = 17;  // for things like variables, queue
-
-  // -------------------------------------------------------------------------
-  // Non-standard options.
-
-  // By default, all inputs to an Op must be initialized Tensors.  Ops
-  // that may initialize tensors for the first time should set this
-  // field to true, to allow the Op to take an uninitialized Tensor as
-  // input.
-  bool allows_uninitialized_input = 19;  // for Assign, etc.
-};
-
-// Information about version-dependent deprecation of an op
-message OpDeprecation {
-  // First GraphDef version at which the op is disallowed.
-  int32 version = 1;
-
-  // Explanation of why it was deprecated and what to use instead.
-  string explanation = 2;
-};
-
-// A collection of OpDefs
-message OpList {
-  repeated OpDef op = 1;
-};
diff --git a/flink-tensorflow/src/main/proto/tensorflow/core/framework/resource_handle.proto b/flink-tensorflow/src/main/proto/tensorflow/core/framework/resource_handle.proto
deleted file mode 100644
index f9f19ca..0000000
--- a/flink-tensorflow/src/main/proto/tensorflow/core/framework/resource_handle.proto
+++ /dev/null
@@ -1,29 +0,0 @@
-syntax = "proto3";
-
-package tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "ResourceHandleProto";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-// Protocol buffer representing a handle to a tensorflow resource. Handles are
-// not valid across executions, but can be serialized back and forth from within
-// a single run.
-message ResourceHandle {
-  // Unique name for the device containing the resource.
-  string device = 1;
-
-  // Container in which this resource is placed.
-  string container = 2;
-
-  // Unique name of this resource.
-  string name = 3;
-
-  // Hash code for the type of the resource. Is only valid in the same device
-  // and in the same execution.
-  uint64 hash_code = 4;
-
-  // For debug-only, the name of the type pointed to by this handle, if
-  // available.
-  string maybe_type_name = 5;
-};
diff --git a/flink-tensorflow/src/main/proto/tensorflow/core/framework/step_stats.proto b/flink-tensorflow/src/main/proto/tensorflow/core/framework/step_stats.proto
deleted file mode 100644
index 4488f98..0000000
--- a/flink-tensorflow/src/main/proto/tensorflow/core/framework/step_stats.proto
+++ /dev/null
@@ -1,54 +0,0 @@
-syntax = "proto3";
-
-package tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "StepStatsProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "tensorflow/core/framework/allocation_description.proto";
-import "tensorflow/core/framework/tensor_description.proto";
-
-// TODO(tucker): The next 4 message defs are very similar to
-// the *LogEntry messages in profile.proto.  They should be
-// unified in one place.
-
-message AllocatorMemoryUsed {
-  string allocator_name = 1;
-  int64 total_bytes = 2;
-  int64 peak_bytes = 3;
-}
-
-// Output sizes recorded for a single execution of a graph node.
-message NodeOutput {
-  int32 slot = 1;
-  TensorDescription tensor_description = 3;
-};
-
-// Time/size stats recorded for a single execution of a graph node.
-message NodeExecStats {
-  // TODO(tucker): Use some more compact form of node identity than
-  // the full string name.  Either all processes should agree on a
-  // global id (cost_id?) for each node, or we should use a hash of
-  // the name.
-  string node_name = 1;
-  int64 all_start_micros = 2;
-  int64 op_start_rel_micros = 3;
-  int64 op_end_rel_micros = 4;
-  int64 all_end_rel_micros = 5;
-  repeated AllocatorMemoryUsed memory = 6;
-  repeated NodeOutput output = 7;
-  string timeline_label = 8;
-  int64 scheduled_micros = 9;
-  uint32 thread_id = 10;
-  repeated AllocationDescription referenced_tensor = 11;
-};
-
-message DeviceStepStats {
-  string device = 1;
-  repeated NodeExecStats node_stats = 2;
-}
-
-message StepStats {
-  repeated DeviceStepStats dev_stats = 1;
-};
diff --git a/flink-tensorflow/src/main/proto/tensorflow/core/framework/summary.proto b/flink-tensorflow/src/main/proto/tensorflow/core/framework/summary.proto
deleted file mode 100644
index 3560b96..0000000
--- a/flink-tensorflow/src/main/proto/tensorflow/core/framework/summary.proto
+++ /dev/null
@@ -1,103 +0,0 @@
-syntax = "proto3";
-
-package tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "SummaryProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "tensorflow/core/framework/tensor.proto";
-
-// Metadata associated with a series of Summary data
-message SummaryDescription {
-  // Hint on how plugins should process the data in this series.
-  // Supported values include "scalar", "histogram", "image", "audio"
-  string type_hint = 1;
-}
-
-// Serialization format for histogram module in
-// core/lib/histogram/histogram.h
-message HistogramProto {
-  double min = 1;
-  double max = 2;
-  double num = 3;
-  double sum = 4;
-  double sum_squares = 5;
-
-  // Parallel arrays encoding the bucket boundaries and the bucket values.
-  // bucket(i) is the count for the bucket i.  The range for
-  // a bucket is:
-  //   i == 0:  -DBL_MAX .. bucket_limit(0)
-  //   i != 0:  bucket_limit(i-1) .. bucket_limit(i)
-  repeated double bucket_limit = 6 [packed = true];
-  repeated double bucket = 7 [packed = true];
-};
-
-// A Summary is a set of named values to be displayed by the
-// visualizer.
-//
-// Summaries are produced regularly during training, as controlled by
-// the "summary_interval_secs" attribute of the training operation.
-// Summaries are also produced at the end of an evaluation.
-message Summary {
-  message Image {
-    // Dimensions of the image.
-    int32 height = 1;
-    int32 width = 2;
-    // Valid colorspace values are
-    //   1 - grayscale
-    //   2 - grayscale + alpha
-    //   3 - RGB
-    //   4 - RGBA
-    //   5 - DIGITAL_YUV
-    //   6 - BGRA
-    int32 colorspace = 3;
-    // Image data in encoded format.  All image formats supported by
-    // image_codec::CoderUtil can be stored here.
-    bytes encoded_image_string = 4;
-  }
-
-  message Audio {
-    // Sample rate of the audio in Hz.
-    float sample_rate = 1;
-    // Number of channels of audio.
-    int64 num_channels = 2;
-    // Length of the audio in frames (samples per channel).
-    int64 length_frames = 3;
-    // Encoded audio data and its associated RFC 2045 content type (e.g.
-    // "audio/wav").
-    bytes encoded_audio_string = 4;
-    string content_type = 5;
-  }
-
-  message Value {
-    // Name of the node that output this summary; in general, the name of a
-    // TensorSummary node. If the node in question has multiple outputs, then
-    // a ":\d+" suffix will be appended, like "some_op:13".
-    // Might not be set for legacy summaries (i.e. those not using the tensor
-    // value field)
-    string node_name = 7;
-
-    // Tag name for the data.  Will only be used by legacy summaries
-    // (ie. those not using the tensor value field)
-    // For legacy summaries, will be used as the title of the graph
-    // in the visualizer.
-    //
-    // Tag is usually "op_name:value_name", where "op_name" itself can have
-    // structure to indicate grouping.
-    string tag = 1;
-
-    // Value associated with the tag.
-    oneof value {
-      float simple_value = 2;
-      bytes obsolete_old_style_histogram = 3;
-      Image image = 4;
-      HistogramProto histo = 5;
-      Audio audio = 6;
-      TensorProto tensor = 8;
-    }
-  }
-
-  // Set of values for the summary.
-  repeated Value value = 1;
-}
diff --git a/flink-tensorflow/src/main/proto/tensorflow/core/framework/tensor.proto b/flink-tensorflow/src/main/proto/tensorflow/core/framework/tensor.proto
deleted file mode 100644
index 5d383bc..0000000
--- a/flink-tensorflow/src/main/proto/tensorflow/core/framework/tensor.proto
+++ /dev/null
@@ -1,75 +0,0 @@
-syntax = "proto3";
-
-package tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "TensorProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "tensorflow/core/framework/resource_handle.proto";
-import "tensorflow/core/framework/tensor_shape.proto";
-import "tensorflow/core/framework/types.proto";
-
-// Protocol buffer representing a tensor.
-message TensorProto {
-  DataType dtype = 1;
-
-  // Shape of the tensor.  TODO(touts): sort out the 0-rank issues.
-  TensorShapeProto tensor_shape = 2;
-
-  // Only one of the representations below is set, one of "tensor_contents" and
-  // the "xxx_val" attributes.  We are not using oneof because as oneofs cannot
-  // contain repeated fields it would require another extra set of messages.
-
-  // Version number.
-  //
-  // In version 0, if the "repeated xxx" representations contain only one
-  // element, that element is repeated to fill the shape.  This makes it easy
-  // to represent a constant Tensor with a single value.
-  int32 version_number = 3;
-
-  // Serialized raw tensor content from either Tensor::AsProtoTensorContent or
-  // memcpy in tensorflow::grpc::EncodeTensorToByteBuffer. This representation
-  // can be used for all tensor types. The purpose of this representation is to
-  // reduce serialization overhead during RPC call by avoiding serialization of
-  // many repeated small items.
-  bytes tensor_content = 4;
-
-  // Type specific representations that make it easy to create tensor protos in
-  // all languages.  Only the representation corresponding to "dtype" can
-  // be set.  The values hold the flattened representation of the tensor in
-  // row major order.
-
-  // DT_HALF. Note that since protobuf has no int16 type, we'll have some
-  // pointless zero padding for each value here.
-  repeated int32 half_val = 13 [packed = true];
-
-  // DT_FLOAT.
-  repeated float float_val = 5 [packed = true];
-
-  // DT_DOUBLE.
-  repeated double double_val = 6 [packed = true];
-
-  // DT_INT32, DT_INT16, DT_INT8, DT_UINT8.
-  repeated int32 int_val = 7 [packed = true];
-
-  // DT_STRING
-  repeated bytes string_val = 8;
-
-  // DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real
-  // and imaginary parts of i-th single precision complex.
-  repeated float scomplex_val = 9 [packed = true];
-
-  // DT_INT64
-  repeated int64 int64_val = 10 [packed = true];
-
-  // DT_BOOL
-  repeated bool bool_val = 11 [packed = true];
-
-  // DT_COMPLEX128. dcomplex_val(2*i) and dcomplex_val(2*i+1) are real
-  // and imaginary parts of i-th double precision complex.
-  repeated double dcomplex_val = 12 [packed = true];
-
-  // DT_RESOURCE
-  repeated ResourceHandle resource_handle_val = 14;
-};
diff --git a/flink-tensorflow/src/main/proto/tensorflow/core/framework/tensor_description.proto b/flink-tensorflow/src/main/proto/tensorflow/core/framework/tensor_description.proto
deleted file mode 100644
index 6ac3c1b..0000000
--- a/flink-tensorflow/src/main/proto/tensorflow/core/framework/tensor_description.proto
+++ /dev/null
@@ -1,22 +0,0 @@
-syntax = "proto3";
-
-package tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "TensorDescriptionProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "tensorflow/core/framework/types.proto";
-import "tensorflow/core/framework/tensor_shape.proto";
-import "tensorflow/core/framework/allocation_description.proto";
-
-message TensorDescription {
-  // Data type of tensor elements
-  DataType dtype = 1;
-
-  // Shape of the tensor.
-  TensorShapeProto shape = 2;
-
-  // Information about the size and allocator used for the data
-  AllocationDescription allocation_description = 4;
-};
diff --git a/flink-tensorflow/src/main/proto/tensorflow/core/framework/tensor_shape.proto b/flink-tensorflow/src/main/proto/tensorflow/core/framework/tensor_shape.proto
deleted file mode 100644
index 1ec3c53..0000000
--- a/flink-tensorflow/src/main/proto/tensorflow/core/framework/tensor_shape.proto
+++ /dev/null
@@ -1,45 +0,0 @@
-// Protocol buffer representing the shape of tensors.
-
-syntax = "proto3";
-option cc_enable_arenas = true;
-option java_outer_classname = "TensorShapeProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-package tensorflow;
-
-// Dimensions of a tensor.
-message TensorShapeProto {
-  // One dimension of the tensor.
-  message Dim {
-    // Size of the tensor in that dimension.
-    // This value must be >= -1, but values of -1 are reserved for "unknown"
-    // shapes (values of -1 mean "unknown" dimension).  Certain wrappers
-    // that work with TensorShapeProto may fail at runtime when deserializing
-    // a TensorShapeProto containing a dim value of -1.
-    int64 size = 1;
-
-    // Optional name of the tensor dimension.
-    string name = 2;
-  };
-
-  // Dimensions of the tensor, such as {"input", 30}, {"output", 40}
-  // for a 30 x 40 2D tensor.  If an entry has size -1, this
-  // corresponds to a dimension of unknown size. The names are
-  // optional.
-  //
-  // The order of entries in "dim" matters: It indicates the layout of the
-  // values in the tensor in-memory representation.
-  //
-  // The first entry in "dim" is the outermost dimension used to layout the
-  // values, the last entry is the innermost dimension.  This matches the
-  // in-memory layout of RowMajor Eigen tensors.
-  //
-  // If "dim.size()" > 0, "unknown_rank" must be false.
-  repeated Dim dim = 2;
-
-  // If true, the number of dimensions in the shape is unknown.
-  //
-  // If true, "dim.size()" must be 0.
-  bool unknown_rank = 3;
-};
diff --git a/flink-tensorflow/src/main/proto/tensorflow/core/framework/tensor_slice.proto b/flink-tensorflow/src/main/proto/tensorflow/core/framework/tensor_slice.proto
deleted file mode 100644
index 24b0166..0000000
--- a/flink-tensorflow/src/main/proto/tensorflow/core/framework/tensor_slice.proto
+++ /dev/null
@@ -1,37 +0,0 @@
-// Protocol buffer representing slices of a tensor
-
-syntax = "proto3";
-option cc_enable_arenas = true;
-option java_outer_classname = "TensorSliceProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-package tensorflow;
-
-// Can only be interpreted if you know the corresponding TensorShape.
-message TensorSliceProto {
-  // Extent of the slice in one dimension.
-  message Extent {
-    // Either both or no attributes must be set.  When no attribute is set
-    // means: All data in that dimension.
-
-    // Start index of the slice, starting at 0.
-    int64 start = 1;
-
-    // Length of the slice: if the length is missing or -1 we will
-    // interpret this as "everything in this dimension".  We use
-    // "oneof" to preserve information about whether the length is
-    // present without changing the serialization format from the
-    // prior proto2 version of this proto.
-    oneof has_length {
-      int64 length = 2;
-    }
-  };
-
-  // Extent of the slice in all tensor dimensions.
-  //
-  // Must have one entry for each of the dimension of the tensor that this
-  // slice belongs to.  The order of sizes is the same as the order of
-  // dimensions in the TensorShape.
-  repeated Extent extent = 1;
-};
diff --git a/flink-tensorflow/src/main/proto/tensorflow/core/framework/types.proto b/flink-tensorflow/src/main/proto/tensorflow/core/framework/types.proto
deleted file mode 100644
index b80e2b3..0000000
--- a/flink-tensorflow/src/main/proto/tensorflow/core/framework/types.proto
+++ /dev/null
@@ -1,64 +0,0 @@
-syntax = "proto3";
-
-package tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "TypesProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-// LINT.IfChange
-enum DataType {
-  // Not a legal value for DataType.  Used to indicate a DataType field
-  // has not been set.
-  DT_INVALID = 0;
-
-  // Data types that all computation devices are expected to be
-  // capable to support.
-  DT_FLOAT = 1;
-  DT_DOUBLE = 2;
-  DT_INT32 = 3;
-  DT_UINT8 = 4;
-  DT_INT16 = 5;
-  DT_INT8 = 6;
-  DT_STRING = 7;
-  DT_COMPLEX64 = 8;  // Single-precision complex
-  DT_INT64 = 9;
-  DT_BOOL = 10;
-  DT_QINT8 = 11;     // Quantized int8
-  DT_QUINT8 = 12;    // Quantized uint8
-  DT_QINT32 = 13;    // Quantized int32
-  DT_BFLOAT16 = 14;  // Float32 truncated to 16 bits.  Only for cast ops.
-  DT_QINT16 = 15;    // Quantized int16
-  DT_QUINT16 = 16;   // Quantized uint16
-  DT_UINT16 = 17;
-  DT_COMPLEX128 = 18;  // Double-precision complex
-  DT_HALF = 19;
-  DT_RESOURCE = 20;
-
-  // TODO(josh11b): DT_GENERIC_PROTO = ??;
-  // TODO(jeff,josh11b): DT_UINT64?  DT_UINT32?
-
-  // Do not use!  These are only for parameters.  Every enum above
-  // should have a corresponding value below (verified by types_test).
-  DT_FLOAT_REF = 101;
-  DT_DOUBLE_REF = 102;
-  DT_INT32_REF = 103;
-  DT_UINT8_REF = 104;
-  DT_INT16_REF = 105;
-  DT_INT8_REF = 106;
-  DT_STRING_REF = 107;
-  DT_COMPLEX64_REF = 108;
-  DT_INT64_REF = 109;
-  DT_BOOL_REF = 110;
-  DT_QINT8_REF = 111;
-  DT_QUINT8_REF = 112;
-  DT_QINT32_REF = 113;
-  DT_BFLOAT16_REF = 114;
-  DT_QINT16_REF = 115;
-  DT_QUINT16_REF = 116;
-  DT_UINT16_REF = 117;
-  DT_COMPLEX128_REF = 118;
-  DT_HALF_REF = 119;
-  DT_RESOURCE_REF = 120;
-}
-// LINT.ThenChange(https://www.tensorflow.org/code/tensorflow/c/c_api.h,https://www.tensorflow.org/code/tensorflow/go/tensor.go)
diff --git a/flink-tensorflow/src/main/proto/tensorflow/core/framework/variable.proto b/flink-tensorflow/src/main/proto/tensorflow/core/framework/variable.proto
deleted file mode 100644
index e793f5a..0000000
--- a/flink-tensorflow/src/main/proto/tensorflow/core/framework/variable.proto
+++ /dev/null
@@ -1,33 +0,0 @@
-syntax = "proto3";
-
-package tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "VariableProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-// Protocol buffer representing a Variable.
-message VariableDef {
-  // Name of the variable tensor.
-  string variable_name = 1;
-
-  // Name of the initializer op.
-  string initializer_name = 2;
-
-  // Name of the snapshot tensor.
-  string snapshot_name = 3;
-
-  // Support for saving variables as slices of a larger variable.
-  SaveSliceInfoDef save_slice_info_def = 4;
-}
-
-message SaveSliceInfoDef {
-  // Name of the full variable of which this is a slice.
-  string full_name = 1;
-  // Shape of the full variable.
-  repeated int64 full_shape = 2;
-  // Offset of this variable into the full variable.
-  repeated int64 var_offset = 3;
-  // Shape of this variable.
-  repeated int64 var_shape = 4;
-}
diff --git a/flink-tensorflow/src/main/proto/tensorflow/core/framework/versions.proto b/flink-tensorflow/src/main/proto/tensorflow/core/framework/versions.proto
deleted file mode 100644
index 7d5e58a..0000000
--- a/flink-tensorflow/src/main/proto/tensorflow/core/framework/versions.proto
+++ /dev/null
@@ -1,31 +0,0 @@
-syntax = "proto3";
-
-package tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "VersionsProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-// Version information for a piece of serialized data
-//
-// There are different types of versions for each type of data
-// (GraphDef, etc.), but they all have the same common shape
-// described here.
-//
-// Each consumer has "consumer" and "min_producer" versions (specified
-// elsewhere).  A consumer is allowed to consume this data if
-//
-//   producer >= min_producer
-//   consumer >= min_consumer
-//   consumer not in bad_consumers
-//
-message VersionDef {
-  // The version of the code that produced this data.
-  int32 producer = 1;
-
-  // Any consumer below this version is not allowed to consume this data.
-  int32 min_consumer = 2;
-
-  // Specific consumer versions which are disallowed (e.g. due to bugs).
-  repeated int32 bad_consumers = 3;
-};
diff --git a/flink-tensorflow/src/main/proto/tensorflow/core/protobuf/meta_graph.proto b/flink-tensorflow/src/main/proto/tensorflow/core/protobuf/meta_graph.proto
deleted file mode 100644
index 5b20223..0000000
--- a/flink-tensorflow/src/main/proto/tensorflow/core/protobuf/meta_graph.proto
+++ /dev/null
@@ -1,292 +0,0 @@
-syntax = "proto3";
-
-package tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "MetaGraphProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "google/protobuf/any.proto";
-
-import "tensorflow/core/framework/graph.proto";
-import "tensorflow/core/framework/op_def.proto";
-import "tensorflow/core/framework/tensor_shape.proto";
-import "tensorflow/core/framework/types.proto";
-import "tensorflow/core/protobuf/saver.proto";
-
-// NOTE: This protocol buffer is evolving, and will go through revisions in the
-// coming months.
-//
-// Protocol buffer containing the following which are necessary to restart
-// training, run inference. It can be used to serialize/de-serialize memory
-// objects necessary for running computation in a graph when crossing the
-// process boundary. It can be used for long term storage of graphs,
-// cross-language execution of graphs, etc.
-//   MetaInfoDef
-//   GraphDef
-//   SaverDef
-//   CollectionDef
-//   TensorInfo
-//   SignatureDef
-message MetaGraphDef {
-  // Meta information regarding the graph to be exported.  To be used by users
-  // of this protocol buffer to encode information regarding their meta graph.
-  message MetaInfoDef {
-    // User specified Version string. Can be the name of the model and revision,
-    // steps this model has been trained to, etc.
-    string meta_graph_version = 1;
-
-    // A copy of the OpDefs used by the producer of this graph_def.
-    // Descriptions and Ops not used in graph_def are stripped out.
-    OpList stripped_op_list = 2;
-
-    // A serialized protobuf. Can be the time this meta graph is created, or
-    // modified, or name of the model.
-    google.protobuf.Any any_info = 3;
-
-    // User supplied tag(s) on the meta_graph and included graph_def.
-    //
-    // MetaGraphDefs should be tagged with their capabilities or use-cases.
-    // Examples: "train", "serve", "gpu", "tpu", etc.
-    // These tags enable loaders to access the MetaGraph(s) appropriate for a
-    // specific use-case or runtime environment.
-    repeated string tags = 4;
-
-    // The __version__ string of the tensorflow build used to write this graph.
-    // This will be populated by the framework, which will overwrite any user
-    // supplied value.
-    string tensorflow_version = 5;
-
-    // The __git_version__ string of the tensorflow build used to write this
-    // graph. This will be populated by the framework, which will overwrite any
-    // user supplied value.
-    string tensorflow_git_version = 6;
-  }
-  MetaInfoDef meta_info_def = 1;
-
-  // GraphDef.
-  GraphDef graph_def = 2;
-
-  // SaverDef.
-  SaverDef saver_def = 3;
-
-  // collection_def: Map from collection name to collections.
-  // See CollectionDef section for details.
-  map<string, CollectionDef> collection_def = 4;
-
-  // signature_def: Map from user supplied key for a signature to a single
-  // SignatureDef.
-  map<string, SignatureDef> signature_def = 5;
-
-  // Asset file def to be used with the defined graph.
-  repeated AssetFileDef asset_file_def = 6;
-}
-
-// CollectionDef should cover most collections.
-// To add a user-defined collection, do one of the following:
-// 1. For simple data types, such as string, int, float:
-//      tf.add_to_collection("your_collection_name", your_simple_value)
-//    strings will be stored as bytes_list.
-//
-// 2. For Protobuf types, there are three ways to add them:
-//    1) tf.add_to_collection("your_collection_name",
-//         your_proto.SerializeToString())
-//
-//       collection_def {
-//         key: "user_defined_bytes_collection"
-//         value {
-//           bytes_list {
-//             value: "queue_name: \"test_queue\"\n"
-//           }
-//         }
-//       }
-//
-//  or
-//
-//    2) tf.add_to_collection("your_collection_name", str(your_proto))
-//
-//       collection_def {
-//         key: "user_defined_string_collection"
-//         value {
-//          bytes_list {
-//             value: "\n\ntest_queue"
-//           }
-//         }
-//       }
-//
-//  or
-//
-//    3) any_buf = any_pb2.Any()
-//       tf.add_to_collection("your_collection_name",
-//         any_buf.Pack(your_proto))
-//
-//       collection_def {
-//         key: "user_defined_any_collection"
-//         value {
-//           any_list {
-//             value {
-//               type_url: "type.googleapis.com/tensorflow.QueueRunnerDef"
-//               value: "\n\ntest_queue"
-//             }
-//           }
-//         }
-//       }
-//
-// 3. For Python objects, implement to_proto() and from_proto(), and register
-//    them in the following manner:
-//    ops.register_proto_function("your_collection_name",
-//                                proto_type,
-//                                to_proto=YourPythonObject.to_proto,
-//                                from_proto=YourPythonObject.from_proto)
-//    These functions will be invoked to serialize and de-serialize the
-//    collection. For example,
-//    ops.register_proto_function(ops.GraphKeys.GLOBAL_VARIABLES,
-//                                proto_type=variable_pb2.VariableDef,
-//                                to_proto=Variable.to_proto,
-//                                from_proto=Variable.from_proto)
-message CollectionDef {
-  // NodeList is used for collecting nodes in graph. For example
-  // collection_def {
-  //   key: "summaries"
-  //   value {
-  //     node_list {
-  //       value: "input_producer/ScalarSummary:0"
-  //       value: "shuffle_batch/ScalarSummary:0"
-  //       value: "ImageSummary:0"
-  //     }
-  //   }
-  message NodeList {
-    repeated string value = 1;
-  }
-
-  // BytesList is used for collecting strings and serialized protobufs. For
-  // example:
-  // collection_def {
-  //   key: "trainable_variables"
-  //   value {
-  //     bytes_list {
-  //       value: "\n\017conv1/weights:0\022\024conv1/weights/Assign
-  //              \032\024conv1/weights/read:0"
-  //       value: "\n\016conv1/biases:0\022\023conv1/biases/Assign\032
-  //              \023conv1/biases/read:0"
-  //     }
-  //   }
-  // }
-  message BytesList {
-    repeated bytes value = 1;
-  }
-
-  // Int64List is used for collecting int, int64 and long values.
-  message Int64List {
-    repeated int64 value = 1 [packed = true];
-  }
-
-  // FloatList is used for collecting float values.
-  message FloatList {
-    repeated float value = 1 [packed = true];
-  }
-
-  // AnyList is used for collecting Any protos.
-  message AnyList {
-    repeated google.protobuf.Any value = 1;
-  }
-
-  oneof kind {
-    NodeList node_list = 1;
-    BytesList bytes_list = 2;
-    Int64List int64_list = 3;
-    FloatList float_list = 4;
-    AnyList any_list = 5;
-  }
-}
-
-// Information about a Tensor necessary for feeding or retrieval.
-message TensorInfo {
-  string name = 1;
-  DataType dtype = 2;
-  TensorShapeProto tensor_shape = 3;
-}
-
-// SignatureDef defines the signature of a computation supported by a TensorFlow
-// graph.
-//
-// For example, a model with two loss computations, sharing a single input,
-// might have the following signature_def map.
-//
-// Note that across the two SignatureDefs "loss_A" and "loss_B", the input key,
-// output key, and method_name are identical, and will be used by system(s) that
-// implement or rely upon this particular loss method. The output tensor names
-// differ, demonstrating how different outputs can exist for the same method.
-//
-// signature_def {
-//   key: "loss_A"
-//   value {
-//     inputs {
-//       key: "input"
-//       value {
-//         name: "input:0"
-//         dtype: DT_STRING
-//         tensor_shape: ...
-//       }
-//     }
-//     outputs {
-//       key: "loss_output"
-//       value {
-//         name: "loss_output_A:0"
-//         dtype: DT_FLOAT
-//         tensor_shape: ...
-//       }
-//     }
-//   }
-//   ...
-//   method_name: "some/package/compute_loss"
-// }
-// signature_def {
-//   key: "loss_B"
-//   value {
-//     inputs {
-//       key: "input"
-//       value {
-//         name: "input:0"
-//         dtype: DT_STRING
-//         tensor_shape: ...
-//       }
-//     }
-//     outputs {
-//       key: "loss_output"
-//       value {
-//         name: "loss_output_B:0"
-//         dtype: DT_FLOAT
-//         tensor_shape: ...
-//       }
-//     }
-//   }
-//   ...
-//   method_name: "some/package/compute_loss"
-// }
-message SignatureDef {
-  // Named input parameters.
-  map<string, TensorInfo> inputs = 1;
-  // Named output parameters.
-  map<string, TensorInfo> outputs = 2;
-  // Extensible method_name information enabling third-party users to mark a
-  // SignatureDef as supporting a particular method. This enables producers and
-  // consumers of SignatureDefs, e.g. a model definition library and a serving
-  // library to have a clear hand-off regarding the semantics of a computation.
-  //
-  // Note that multiple SignatureDefs in a single MetaGraphDef may have the same
-  // method_name. This is commonly used to support multi-headed computation,
-  // where a single graph computation may return multiple results.
-  string method_name = 3;
-}
-
-// An asset file def for a single file or a set of sharded files with the same
-// name.
-message AssetFileDef {
-  // The tensor to bind the asset filename to.
-  TensorInfo tensor_info = 1;
-  // The filename within an assets directory. Note: does not include the path
-  // prefix, i.e. directories. For an asset at /tmp/path/vocab.txt, the filename
-  // would be "vocab.txt".
-  string filename = 2;
-}
diff --git a/flink-tensorflow/src/main/proto/tensorflow/core/protobuf/saved_model.proto b/flink-tensorflow/src/main/proto/tensorflow/core/protobuf/saved_model.proto
deleted file mode 100644
index c2595dd..0000000
--- a/flink-tensorflow/src/main/proto/tensorflow/core/protobuf/saved_model.proto
+++ /dev/null
@@ -1,21 +0,0 @@
-syntax = "proto3";
-
-package tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "SavedModelProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "tensorflow/core/protobuf/meta_graph.proto";
-
-// SavedModel is the high level serialization format for TensorFlow Models.
-// See [todo: doc links, similar to session_bundle] for more information.
-message SavedModel {
-  // The schema version of the SavedModel instance. Used for versioning when
-  // making future changes to the specification/implementation. Initial value
-  // at release will be 1.
-  int64 saved_model_schema_version = 1;
-
-  // One or more MetaGraphs.
-  repeated MetaGraphDef meta_graphs = 2;
-}
diff --git a/flink-tensorflow/src/main/proto/tensorflow/core/protobuf/saver.proto b/flink-tensorflow/src/main/proto/tensorflow/core/protobuf/saver.proto
deleted file mode 100644
index 65fe9c4..0000000
--- a/flink-tensorflow/src/main/proto/tensorflow/core/protobuf/saver.proto
+++ /dev/null
@@ -1,46 +0,0 @@
-syntax = "proto3";
-
-package tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "SaverProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.util";
-
-// Protocol buffer representing the configuration of a Saver.
-message SaverDef {
-  // The name of the tensor in which to specify the filename when saving or
-  // restoring a model checkpoint.
-  string filename_tensor_name = 1;
-
-  // The operation to run when saving a model checkpoint.
-  string save_tensor_name = 2;
-
-  // The operation to run when restoring a model checkpoint.
-  string restore_op_name = 3;
-
-  // Maximum number of checkpoints to keep.  If 0, no checkpoints are deleted.
-  int32 max_to_keep = 4;
-
-  // Shard the save files, one per device that has Variable nodes.
-  bool sharded = 5;
-
-  // How often to keep an additional checkpoint. If not specified, only the last
-  // "max_to_keep" checkpoints are kept; if specified, in addition to keeping
-  // the last "max_to_keep" checkpoints, an additional checkpoint will be kept
-  // for every n hours of training.
-  float keep_checkpoint_every_n_hours = 6;
-
-  // A version number that identifies a different on-disk checkpoint format.
-  // Usually, each subclass of BaseSaverBuilder works with a particular
-  // version/format.  However, it is possible that the same builder may be
-  // upgraded to support a newer checkpoint format in the future.
-  enum CheckpointFormatVersion {
-    // Internal legacy format.
-    LEGACY = 0;
-    // Current format: tf.Saver() which works with tensorflow::table::Table.
-    V1 = 1;
-    // Experimental format under development.
-    V2 = 2;
-  }
-  CheckpointFormatVersion version = 7;
-}
diff --git a/flink-tensorflow/src/main/scala/org/apache/flink/contrib/tensorflow/ml/signatures/PredictionMethod.scala b/flink-tensorflow/src/main/scala/org/apache/flink/contrib/tensorflow/ml/signatures/PredictionMethod.scala
new file mode 100644
index 0000000..2a78894
--- /dev/null
+++ b/flink-tensorflow/src/main/scala/org/apache/flink/contrib/tensorflow/ml/signatures/PredictionMethod.scala
@@ -0,0 +1,29 @@
+package org.apache.flink.contrib.tensorflow.ml.signatures
+
+import org.apache.flink.contrib.tensorflow.graphs.GraphMethod
+import org.apache.flink.contrib.tensorflow.models.savedmodel.SignatureConstants._
+import org.tensorflow.Tensor
+
+/**
+  * The standard prediction signature.
+  *
+  * See https://github.com/tensorflow/serving/blob/master/tensorflow_serving/servables/tensorflow/predict_impl.cc
+  */
+sealed trait PredictionMethod extends GraphMethod {
+  val name = PREDICT_METHOD_NAME
+  override type Input = Tensor
+  override type Output = Tensor
+}
+
+object PredictionMethod {
+
+  /**
+    * For each input tensor, output the score for each possible classification
+    */
+  implicit val impl = new PredictionMethod {
+    type Result = Tensor
+    def inputs(i: Tensor): Map[String, Tensor] = Map(PREDICT_INPUTS -> i)
+    def outputs(o: Map[String, Tensor]): Tensor = o(PREDICT_OUTPUTS)
+  }
+}
+
diff --git a/flink-tensorflow/src/main/scala/org/apache/flink/contrib/tensorflow/util/RegistrationUtils.java b/flink-tensorflow/src/main/scala/org/apache/flink/contrib/tensorflow/util/RegistrationUtils.java
index 77e3b25..987b781 100644
--- a/flink-tensorflow/src/main/scala/org/apache/flink/contrib/tensorflow/util/RegistrationUtils.java
+++ b/flink-tensorflow/src/main/scala/org/apache/flink/contrib/tensorflow/util/RegistrationUtils.java
@@ -70,7 +70,7 @@ public static void registerTypes(ExecutionConfig config) {
 		config.registerTypeWithKryoSerializer(MemoryLogRawAllocation.class, ProtobufSerializer.class);
 		config.registerTypeWithKryoSerializer(SummaryDescription.class, ProtobufSerializer.class);
 		config.registerTypeWithKryoSerializer(Summary.Value.class, ProtobufSerializer.class);
-		config.registerTypeWithKryoSerializer(FunctionDef.Node.class, ProtobufSerializer.class);
+//		config.registerTypeWithKryoSerializer(FunctionDef.Node.class, ProtobufSerializer.class);
 		config.registerTypeWithKryoSerializer(DeviceStepStats.class, ProtobufSerializer.class);
 		config.registerTypeWithKryoSerializer(MemoryLogTensorOutput.class, ProtobufSerializer.class);
 		config.registerTypeWithKryoSerializer(AttrValue.class, ProtobufSerializer.class);
diff --git a/flink-tensorflow/src/test/scala/org/apache/flink/contrib/tensorflow/ml/MNIST_dense.scala b/flink-tensorflow/src/test/scala/org/apache/flink/contrib/tensorflow/ml/MNIST_dense.scala
new file mode 100644
index 0000000..f144b9d
--- /dev/null
+++ b/flink-tensorflow/src/test/scala/org/apache/flink/contrib/tensorflow/ml/MNIST_dense.scala
@@ -0,0 +1,23 @@
+package org.apache.flink.contrib.tensorflow.ml
+
+import org.apache.flink.contrib.tensorflow.ml.signatures.PredictionMethod
+import org.apache.flink.contrib.tensorflow.models.ModelFunction
+import org.apache.flink.contrib.tensorflow.models.savedmodel.TensorFlowModel
+import org.apache.flink.contrib.tensorflow.models.savedmodel.TensorFlowModel._
+import org.apache.flink.core.fs.Path
+
+/**
+  * The MNIST_dense model.
+ *
+  * @param modelPath path to the saved model data.
+  */
+@SerialVersionUID(1L)
+class MNIST_dense(modelPath: Path) extends TensorFlowModel[MNIST_dense] {
+
+  override protected val loader = load(modelPath, "serve")
+
+  /**
+    * Prediction returns scores for all classes
+    */
+  def predict = ModelFunction[PredictionMethod](session(), signatureDef("predict_images").get)
+}
diff --git a/flink-tensorflow/src/test/scala/org/apache/flink/contrib/tensorflow/ml/PredictITCase.scala b/flink-tensorflow/src/test/scala/org/apache/flink/contrib/tensorflow/ml/PredictITCase.scala
new file mode 100644
index 0000000..fdb3eb6
--- /dev/null
+++ b/flink-tensorflow/src/test/scala/org/apache/flink/contrib/tensorflow/ml/PredictITCase.scala
@@ -0,0 +1,97 @@
+package org.apache.flink.contrib.tensorflow.ml
+
+import com.twitter.bijection.Conversion._
+import org.apache.flink.api.common.functions.RichFlatMapFunction
+import org.apache.flink.api.scala._
+import org.apache.flink.configuration.Configuration
+import org.apache.flink.contrib.tensorflow.types.{TensorValue, TensorValueBuilder}
+import org.apache.flink.contrib.tensorflow.util.{FlinkTestBase, RegistrationUtils}
+import org.apache.flink.core.fs.Path
+import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
+import org.apache.flink.util.Collector
+import org.apache.flink.util.Preconditions.checkState
+import org.junit.runner.RunWith
+import org.scalatest.junit.JUnitRunner
+import org.scalatest.{Matchers, WordSpecLike}
+import org.tensorflow.contrib.scala.Arrays._
+import org.tensorflow.contrib.scala.Rank._
+import org.tensorflow.contrib.scala._
+
+import resource.{managed, _}
+import org.tensorflow.DataType
+
+@RunWith(classOf[JUnitRunner])
+class PredictITCase extends WordSpecLike
+  with Matchers
+  with FlinkTestBase {
+
+  override val parallelism = 1
+
+  type ImageTensorValue = TensorValue[`2D`, Float]
+  type LabeledImageTensorValue = (ImageTensorValue, Int)
+
+
+  def examples(): Seq[LabeledImageTensorValue] = {
+    val num_samples = 5
+    val samples : Array[Array[Float]] = new Array[Array[Float]](num_samples)
+    val labels = new Array[Int](num_samples)
+    labels(0) = 7
+    labels(1) = 2
+    labels(2) = 1
+    labels(3) = 0
+    labels(4) = 4
+    samples(0) = Array(0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,84f,185f,159f,151f,60f,36f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,222f,254f,254f,254f,254f,241f,198f,198f,198f,198f,198f,198f,198f,198f,170f,52f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,67f,114f,72f,114f,163f,227f,254f,225f,254f,254f,254f,250f,229f,254f,254f,140f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,17f,66f,14f,67f,67f,67f,59f,21f,236f,254f,106f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,83f,253f,209f,18f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,22f,233f,255f,83f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,129f,254f,238f,44f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,59f,249f,254f,62f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,133f,254f,187f,5f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,9f,205f,248f,58f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,126f,254f,182f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,75f,251f,240f,57f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,19f,221f,254f,166f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,3f,203f,254f,219f,35f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,38f,254f,254f,77f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,31f,224f,254f,115f,1f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,133f,254f,254f,52f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,61f,242f,254f,254f,52f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,121f,254f,254f,219f,40f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,121f,254f,207f,18f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f)
+    samples(1) = Array(0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,116f,125f,171f,255f,255f,150f,93f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,169f,253f,253f,253f,253f,253f,253f,218f,30f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,169f,253f,253f,253f,213f,142f,176f,253f,253f,122f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,52f,250f,253f,210f,32f,12f,0f,6f,206f,253f,140f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,77f,251f,210f,25f,0f,0f,0f,122f,248f,253f,65f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,31f,18f,0f,0f,0f,0f,209f,253f,253f,65f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,117f,247f,253f,198f,10f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,76f,247f,253f,231f,63f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,128f,253f,253f,144f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,176f,246f,253f,159f,12f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,25f,234f,253f,233f,35f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,198f,253f,253f,141f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,78f,248f,253f,189f,12f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,19f,200f,253f,253f,141f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,134f,253f,253f,173f,12f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,248f,253f,253f,25f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,248f,253f,253f,43f,20f,20f,20f,20f,5f,0f,5f,20f,20f,37f,150f,150f,150f,147f,10f,0f,0f,0f,0f,0f,0f,0f,0f,0f,248f,253f,253f,253f,253f,253f,253f,253f,168f,143f,166f,253f,253f,253f,253f,253f,253f,253f,123f,0f,0f,0f,0f,0f,0f,0f,0f,0f,174f,253f,253f,253f,253f,253f,253f,253f,253f,253f,253f,253f,249f,247f,247f,169f,117f,117f,57f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,118f,123f,123f,123f,166f,253f,253f,253f,155f,123f,123f,41f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f)
+    samples(2) = Array(0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,38f,254f,109f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,87f,252f,82f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,135f,241f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,45f,244f,150f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,84f,254f,63f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,202f,223f,11f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,32f,254f,216f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,95f,254f,195f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,140f,254f,77f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,57f,237f,205f,8f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,124f,255f,165f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,171f,254f,81f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,24f,232f,215f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,120f,254f,159f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,151f,254f,142f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,228f,254f,66f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,61f,251f,254f,66f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,141f,254f,205f,3f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,10f,215f,254f,121f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,5f,198f,176f,10f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f)
+    samples(3) = Array(0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,11f,150f,253f,202f,31f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,37f,251f,251f,253f,107f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,21f,197f,251f,251f,253f,107f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,110f,190f,251f,251f,251f,253f,169f,109f,62f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,253f,251f,251f,251f,251f,253f,251f,251f,220f,51f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,182f,255f,253f,253f,253f,253f,234f,222f,253f,253f,253f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,63f,221f,253f,251f,251f,251f,147f,77f,62f,128f,251f,251f,105f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,32f,231f,251f,253f,251f,220f,137f,10f,0f,0f,31f,230f,251f,243f,113f,5f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,37f,251f,251f,253f,188f,20f,0f,0f,0f,0f,0f,109f,251f,253f,251f,35f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,37f,251f,251f,201f,30f,0f,0f,0f,0f,0f,0f,31f,200f,253f,251f,35f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,37f,253f,253f,0f,0f,0f,0f,0f,0f,0f,0f,32f,202f,255f,253f,164f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,140f,251f,251f,0f,0f,0f,0f,0f,0f,0f,0f,109f,251f,253f,251f,35f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,217f,251f,251f,0f,0f,0f,0f,0f,0f,21f,63f,231f,251f,253f,230f,30f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,217f,251f,251f,0f,0f,0f,0f,0f,0f,144f,251f,251f,251f,221f,61f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,217f,251f,251f,0f,0f,0f,0f,0f,182f,221f,251f,251f,251f,180f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,218f,253f,253f,73f,73f,228f,253f,253f,255f,253f,253f,253f,253f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,113f,251f,251f,253f,251f,251f,251f,251f,253f,251f,251f,251f,147f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,31f,230f,251f,253f,251f,251f,251f,251f,253f,230f,189f,35f,10f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,62f,142f,253f,251f,251f,251f,251f,253f,107f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,72f,174f,251f,173f,71f,72f,30f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f)
+    samples(4) = Array(0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,50f,224f,0f,0f,0f,0f,0f,0f,0f,70f,29f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,121f,231f,0f,0f,0f,0f,0f,0f,0f,148f,168f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,4f,195f,231f,0f,0f,0f,0f,0f,0f,0f,96f,210f,11f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,69f,252f,134f,0f,0f,0f,0f,0f,0f,0f,114f,252f,21f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,45f,236f,217f,12f,0f,0f,0f,0f,0f,0f,0f,192f,252f,21f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,168f,247f,53f,0f,0f,0f,0f,0f,0f,0f,18f,255f,253f,21f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,84f,242f,211f,0f,0f,0f,0f,0f,0f,0f,0f,141f,253f,189f,5f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,169f,252f,106f,0f,0f,0f,0f,0f,0f,0f,32f,232f,250f,66f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,15f,225f,252f,0f,0f,0f,0f,0f,0f,0f,0f,134f,252f,211f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,22f,252f,164f,0f,0f,0f,0f,0f,0f,0f,0f,169f,252f,167f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,9f,204f,209f,18f,0f,0f,0f,0f,0f,0f,22f,253f,253f,107f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,169f,252f,199f,85f,85f,85f,85f,129f,164f,195f,252f,252f,106f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,41f,170f,245f,252f,252f,252f,252f,232f,231f,251f,252f,252f,9f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,49f,84f,84f,84f,84f,0f,0f,161f,252f,252f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,127f,252f,252f,45f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,128f,253f,253f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,127f,252f,252f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,135f,252f,244f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,232f,236f,111f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,179f,66f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f,0f)
+
+    val shapeArray = Array(1L, 784L)
+
+    val tvb = TensorValueBuilder.newBuilder()
+    tvb.shape(shapeArray)
+    tvb.dataType(DataType.FLOAT)
+
+    for (i <- 0 to (num_samples - 1))
+      yield (tvb.data(samples(i)).build().asInstanceOf[ImageTensorValue], labels(i))
+  }
+
+
+  "A PredictFunction" should {
+    "process elements" in {
+      val env = StreamExecutionEnvironment.getExecutionEnvironment
+      RegistrationUtils.registerTypes(env.getConfig)
+
+      val model = new MNIST_dense(new Path("../models/mnist_dense"))
+
+
+      val outputs = env
+        .fromCollection(examples())
+        .flatMap(new RichFlatMapFunction[LabeledImageTensorValue, Int] {
+          override def open(parameters: Configuration): Unit = model.open()
+          override def close(): Unit = model.close()
+
+          override def flatMap(value: LabeledImageTensorValue, out: Collector[Int]): Unit = {
+
+            for {
+              x <- managed(value._1.toTensor())
+              y <- model.predict(x)
+            } {
+              // cast as a 1D tensor to use the available conversion
+              val o = y.taggedAs[TypedTensor[`1D`,Float]].as[Array[Float]]
+
+              // prediction is the index of the max value returned
+              val max_index = o.zipWithIndex.maxBy(_._1)._2
+
+              checkState(max_index == value._2)
+
+              out.collect(max_index)
+            }
+          }
+        })
+        .print()
+
+      env.execute()
+    }
+  }
+}
diff --git a/models/mnist_dense/saved_model.pb b/models/mnist_dense/saved_model.pb
new file mode 100644
index 0000000..09cc1c4
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diff --git a/models/mnist_dense/variables/variables.data-00000-of-00001 b/models/mnist_dense/variables/variables.data-00000-of-00001
new file mode 100644
index 0000000..d34bc9f
Binary files /dev/null and b/models/mnist_dense/variables/variables.data-00000-of-00001 differ
diff --git a/models/mnist_dense/variables/variables.index b/models/mnist_dense/variables/variables.index
new file mode 100644
index 0000000..a5136fe
Binary files /dev/null and b/models/mnist_dense/variables/variables.index differ