diff --git a/tensorflow-core/tensorflow-core-api/build.sh b/tensorflow-core/tensorflow-core-api/build.sh
index b1c5194214f..895832f3df1 100755
--- a/tensorflow-core/tensorflow-core-api/build.sh
+++ b/tensorflow-core/tensorflow-core-api/build.sh
@@ -79,15 +79,18 @@ fi
 GEN_SRCS_DIR=src/gen/java
 mkdir -p $GEN_SRCS_DIR
 
-GEN_RESOURCE_DIR=src/gen/resources/org/tensorflow/op
+GEN_RESOURCE_DIR=src/gen/resources
 mkdir -p $GEN_RESOURCE_DIR
 
 if [[ -z "${SKIP_EXPORT:-}" ]]; then
   # Export op defs
   echo "Exporting Ops"
   $BAZEL_BIN/java_op_exporter \
-      --api_dirs=$BAZEL_SRCS/external/org_tensorflow/tensorflow/core/api_def/base_api,src/bazel/api_def \
-      $TENSORFLOW_LIB > $GEN_RESOURCE_DIR/ops.pb
+      $TENSORFLOW_LIB \
+      $GEN_RESOURCE_DIR/ops.pb \
+      $GEN_RESOURCE_DIR/ops.pbtxt \
+      $BAZEL_SRCS/external/org_tensorflow/tensorflow/core/api_def/base_api \
+      src/bazel/api_def
 else
   echo "Skipping Op export"
 fi
diff --git a/tensorflow-core/tensorflow-core-api/pom.xml b/tensorflow-core/tensorflow-core-api/pom.xml
index ecbb3cca67c..9e5df6de251 100644
--- a/tensorflow-core/tensorflow-core-api/pom.xml
+++ b/tensorflow-core/tensorflow-core-api/pom.xml
@@ -45,15 +45,15 @@
       <artifactId>ndarray</artifactId>
       <version>${project.version}</version>
     </dependency>
-      <dependency>
-        <groupId>org.junit.jupiter</groupId>
-        <artifactId>junit-jupiter-api</artifactId>
-        <scope>test</scope>
-      </dependency>
-      <dependency>
-        <groupId>org.junit.jupiter</groupId>
-        <artifactId>junit-jupiter-engine</artifactId>
-        <scope>test</scope>
+    <dependency>
+      <groupId>org.junit.jupiter</groupId>
+      <artifactId>junit-jupiter-api</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.junit.jupiter</groupId>
+      <artifactId>junit-jupiter-engine</artifactId>
+      <scope>test</scope>
     </dependency>
     <dependency>
       <groupId>org.openjdk.jmh</groupId>
@@ -359,7 +359,7 @@
           <mainClass>org.tensorflow.op.generator.OpGenerator</mainClass>
           <arguments>
             <argument>${project.basedir}/src/gen/java</argument>
-            <argument>${project.basedir}/src/gen/resources/org/tensorflow/op/ops.pb</argument>
+            <argument>${project.basedir}/src/gen/resources/ops.pb</argument>
           </arguments>
         </configuration>
       </plugin>
@@ -423,10 +423,10 @@
           </execution>
         </executions>
         <configuration>
-            <!-- Activate the use of TCP to transmit events to the plugin -->
-            <!-- disabled as it appears to cause intermittent test failures in GitHub Actions
-            <forkNode implementation="org.apache.maven.plugin.surefire.extensions.SurefireForkNodeFactory"/>
-            -->
+          <!-- Activate the use of TCP to transmit events to the plugin -->
+          <!-- disabled as it appears to cause intermittent test failures in GitHub Actions
+          <forkNode implementation="org.apache.maven.plugin.surefire.extensions.SurefireForkNodeFactory"/>
+          -->
           <additionalClasspathElements>
             <additionalClasspathElement>${project.build.directory}/${project.artifactId}-${project.version}-${native.classifier}.jar</additionalClasspathElement>
             <!-- Note: the following path is not accessible in deploying profile, so other libraries like
diff --git a/tensorflow-core/tensorflow-core-api/src/bazel/op_generator/op_export_main.cc b/tensorflow-core/tensorflow-core-api/src/bazel/op_generator/op_export_main.cc
index f66ea31a994..c2f9b06d070 100644
--- a/tensorflow-core/tensorflow-core-api/src/bazel/op_generator/op_export_main.cc
+++ b/tensorflow-core/tensorflow-core-api/src/bazel/op_generator/op_export_main.cc
@@ -15,6 +15,8 @@
 
 #include <string>
 #include <vector>
+#include <iostream>
+#include <fstream>
 #include <stdio.h>
 
 #include "tensorflow/core/framework/op.h"
@@ -43,7 +45,9 @@ const char kUsageHeader[] =
     "The first argument is the location of the tensorflow binary built for TF-"
     "Java.\nFor example, `bazel-out/k8-opt/bin/external/org_tensorflow/tensorfl"
     "ow/libtensorflow_cc.so`.\n\n"
-    "Finally, the `--api_dirs` argument takes a list of comma-separated "
+    "The second and third arguments are the binary and text output files, respectively.\n"
+    "The text output file will not include ApiDefs, like tensorflow's ops.pbtxt.\n\n"
+    "Finally, the rest of the arguments are used as "
     "directories of API definitions can be provided to override default\n"
     "values found in the ops definitions. Directories are ordered by priority "
     "(the last having precedence over the first).\nFor example, `bazel-tensorf"
@@ -86,32 +90,45 @@ Status UpdateOpDefs(OpList* op_list, const std::vector<tensorflow::string>& api_
 // See usage header.
 // Writes an OpList proto to stdout, with each OpDef having its ApiDef in field 100
 int main(int argc, char* argv[]) {
-  tensorflow::string api_dirs_str;
-  std::vector<tensorflow::Flag> flag_list = {
-      tensorflow::Flag(
-          "api_dirs", &api_dirs_str,
-          "List of directories that contain the ops API definitions protos")};
-  tensorflow::string usage = tensorflow::java::kUsageHeader;
-  usage += tensorflow::Flags::Usage(
-      tensorflow::string(argv[0]) + " <ops library paths...>", flag_list);
-  bool parsed_flags_ok = tensorflow::Flags::Parse(&argc, argv, flag_list);
-  tensorflow::port::InitMain(usage.c_str(), &argc, &argv);
-  QCHECK(parsed_flags_ok && argc > 1) << usage;
-  std::vector<tensorflow::string> api_dirs = tensorflow::str_util::Split(
-      api_dirs_str, ",", tensorflow::str_util::SkipEmpty());
+  tensorflow::port::InitMain(tensorflow::java::kUsageHeader, &argc, &argv);
+  std::vector<tensorflow::string> api_dirs;
 
-  tensorflow::Env* env = tensorflow::Env::Default();
-  void* ops_libs_handles[50];
-  for (int i = 1; i < argc; ++i) {
-    TF_CHECK_OK(env->LoadDynamicLibrary(argv[1], &ops_libs_handles[i - 1]));
+  if(argc < 4) {
+      std::cerr << "Must specify <library_path> <binary_output> <text_output>" << "\n";
+      std::cerr << tensorflow::java::kUsageHeader;
+      return 1;
+  }
+  
+  for(int i = 4 ; i < argc ; i++){
+    api_dirs.push_back(argv[i]);
+  }
+
+  std::ofstream binary_output (argv[2], std::ios::out | std::ios::trunc | std::ios::binary);
+  std::ofstream text_output  (argv[3], std::ios::out | std::ios::trunc);
+
+  if(!binary_output.is_open()){
+    std::cerr << "Error opening file " << argv[2] << "\n";
+    return 1;
+  }
+
+  if(!text_output.is_open()){
+    std::cerr << "Error opening file " << argv[3] << "\n";
+    return 1;
   }
+
+  tensorflow::Env* env = tensorflow::Env::Default();
+  void* ops_libs_handles[1];
+  TF_CHECK_OK(env->LoadDynamicLibrary(argv[1], &ops_libs_handles[0]));
   tensorflow::OpList ops;
   tensorflow::OpRegistry::Global()->Export(false, &ops);
-  TF_CHECK_OK(tensorflow::java::UpdateOpDefs(&ops, api_dirs, env));
 
+  text_output << ops.DebugString();
+  text_output.close();
+
+  TF_CHECK_OK(tensorflow::java::UpdateOpDefs(&ops, api_dirs, env));
 
-  std::ostream & out = std::cout;
-  ops.SerializeToOstream(&out);
+  ops.SerializeToOstream(&binary_output);
+  binary_output.close();
 
   return 0;
 }
diff --git a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/Ops.java b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/Ops.java
index 250ea35b9fa..733e7ca7051 100644
--- a/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/Ops.java
+++ b/tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/Ops.java
@@ -354,20 +354,20 @@ public final class Ops {
 
   public final SparseOps sparse;
 
-  public final BitwiseOps bitwise;
-
   public final TpuOps tpu;
 
+  public final BitwiseOps bitwise;
+
   public final MathOps math;
 
   public final AudioOps audio;
 
   public final SignalOps signal;
 
-  public final QuantizationOps quantization;
-
   public final TrainOps train;
 
+  public final QuantizationOps quantization;
+
   private final Scope scope;
 
   private Ops(Scope scope) {
@@ -385,13 +385,13 @@ private Ops(Scope scope) {
     random = new RandomOps(this);
     strings = new StringsOps(this);
     sparse = new SparseOps(this);
-    bitwise = new BitwiseOps(this);
     tpu = new TpuOps(this);
+    bitwise = new BitwiseOps(this);
     math = new MathOps(this);
     audio = new AudioOps(this);
     signal = new SignalOps(this);
-    quantization = new QuantizationOps(this);
     train = new TrainOps(this);
+    quantization = new QuantizationOps(this);
   }
 
   /**
diff --git a/tensorflow-core/tensorflow-core-api/src/gen/resources/ops.pb b/tensorflow-core/tensorflow-core-api/src/gen/resources/ops.pb
new file mode 100644
index 00000000000..e4a333e5977
Binary files /dev/null and b/tensorflow-core/tensorflow-core-api/src/gen/resources/ops.pb differ
diff --git a/tensorflow-core/tensorflow-core-api/src/gen/resources/ops.pbtxt b/tensorflow-core/tensorflow-core-api/src/gen/resources/ops.pbtxt
new file mode 100644
index 00000000000..ba7f37f2ccf
--- /dev/null
+++ b/tensorflow-core/tensorflow-core-api/src/gen/resources/ops.pbtxt
@@ -0,0 +1,58102 @@
+op {
+  name: "Abort"
+  attr {
+    name: "error_msg"
+    type: "string"
+    default_value {
+      s: ""
+    }
+  }
+  attr {
+    name: "exit_without_error"
+    type: "bool"
+    default_value {
+      b: false
+    }
+  }
+}
+op {
+  name: "Abs"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_BFLOAT16
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+}
+op {
+  name: "AccumulateNV2"
+  input_arg {
+    name: "inputs"
+    type_attr: "T"
+    number_attr: "N"
+  }
+  output_arg {
+    name: "sum"
+    type_attr: "T"
+  }
+  attr {
+    name: "N"
+    type: "int"
+    has_minimum: true
+    minimum: 1
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_INT64
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_BFLOAT16
+        type: DT_UINT16
+        type: DT_COMPLEX128
+        type: DT_HALF
+        type: DT_UINT32
+        type: DT_UINT64
+      }
+    }
+  }
+  attr {
+    name: "shape"
+    type: "shape"
+  }
+  is_aggregate: true
+  is_commutative: true
+}
+op {
+  name: "AccumulatorApplyGradient"
+  input_arg {
+    name: "handle"
+    type: DT_STRING
+    is_ref: true
+  }
+  input_arg {
+    name: "local_step"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "gradient"
+    type_attr: "dtype"
+  }
+  attr {
+    name: "dtype"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_INT64
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_BFLOAT16
+        type: DT_UINT16
+        type: DT_COMPLEX128
+        type: DT_HALF
+        type: DT_UINT32
+        type: DT_UINT64
+      }
+    }
+  }
+}
+op {
+  name: "AccumulatorNumAccumulated"
+  input_arg {
+    name: "handle"
+    type: DT_STRING
+    is_ref: true
+  }
+  output_arg {
+    name: "num_accumulated"
+    type: DT_INT32
+  }
+}
+op {
+  name: "AccumulatorSetGlobalStep"
+  input_arg {
+    name: "handle"
+    type: DT_STRING
+    is_ref: true
+  }
+  input_arg {
+    name: "new_global_step"
+    type: DT_INT64
+  }
+}
+op {
+  name: "AccumulatorTakeGradient"
+  input_arg {
+    name: "handle"
+    type: DT_STRING
+    is_ref: true
+  }
+  input_arg {
+    name: "num_required"
+    type: DT_INT32
+  }
+  output_arg {
+    name: "average"
+    type_attr: "dtype"
+  }
+  attr {
+    name: "dtype"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_INT64
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_BFLOAT16
+        type: DT_UINT16
+        type: DT_COMPLEX128
+        type: DT_HALF
+        type: DT_UINT32
+        type: DT_UINT64
+      }
+    }
+  }
+}
+op {
+  name: "Acos"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_BFLOAT16
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+}
+op {
+  name: "Acosh"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_BFLOAT16
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+}
+op {
+  name: "Add"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "z"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_BFLOAT16
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_UINT8
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+        type: DT_STRING
+      }
+    }
+  }
+}
+op {
+  name: "AddManySparseToTensorsMap"
+  input_arg {
+    name: "sparse_indices"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "sparse_values"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "sparse_shape"
+    type: DT_INT64
+  }
+  output_arg {
+    name: "sparse_handles"
+    type: DT_INT64
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  attr {
+    name: "container"
+    type: "string"
+    default_value {
+      s: ""
+    }
+  }
+  attr {
+    name: "shared_name"
+    type: "string"
+    default_value {
+      s: ""
+    }
+  }
+  is_stateful: true
+}
+op {
+  name: "AddN"
+  input_arg {
+    name: "inputs"
+    type_attr: "T"
+    number_attr: "N"
+  }
+  output_arg {
+    name: "sum"
+    type_attr: "T"
+  }
+  attr {
+    name: "N"
+    type: "int"
+    has_minimum: true
+    minimum: 1
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_INT64
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_BFLOAT16
+        type: DT_UINT16
+        type: DT_COMPLEX128
+        type: DT_HALF
+        type: DT_UINT32
+        type: DT_UINT64
+        type: DT_VARIANT
+      }
+    }
+  }
+  is_aggregate: true
+  is_commutative: true
+}
+op {
+  name: "AddSparseToTensorsMap"
+  input_arg {
+    name: "sparse_indices"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "sparse_values"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "sparse_shape"
+    type: DT_INT64
+  }
+  output_arg {
+    name: "sparse_handle"
+    type: DT_INT64
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  attr {
+    name: "container"
+    type: "string"
+    default_value {
+      s: ""
+    }
+  }
+  attr {
+    name: "shared_name"
+    type: "string"
+    default_value {
+      s: ""
+    }
+  }
+  is_stateful: true
+}
+op {
+  name: "AddV2"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "z"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_BFLOAT16
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_UINT8
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_UINT32
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+  is_aggregate: true
+  is_commutative: true
+}
+op {
+  name: "AdjustContrast"
+  input_arg {
+    name: "images"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "contrast_factor"
+    type: DT_FLOAT
+  }
+  input_arg {
+    name: "min_value"
+    type: DT_FLOAT
+  }
+  input_arg {
+    name: "max_value"
+    type: DT_FLOAT
+  }
+  output_arg {
+    name: "output"
+    type: DT_FLOAT
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_UINT8
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_FLOAT
+        type: DT_DOUBLE
+      }
+    }
+  }
+  deprecation {
+    version: 2
+    explanation: "Use AdjustContrastv2 instead"
+  }
+}
+op {
+  name: "AdjustContrastv2"
+  input_arg {
+    name: "images"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "contrast_factor"
+    type: DT_FLOAT
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    default_value {
+      type: DT_FLOAT
+    }
+    allowed_values {
+      list {
+        type: DT_HALF
+        type: DT_FLOAT
+      }
+    }
+  }
+}
+op {
+  name: "AdjustHue"
+  input_arg {
+    name: "images"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "delta"
+    type: DT_FLOAT
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    default_value {
+      type: DT_FLOAT
+    }
+    allowed_values {
+      list {
+        type: DT_HALF
+        type: DT_FLOAT
+      }
+    }
+  }
+}
+op {
+  name: "AdjustSaturation"
+  input_arg {
+    name: "images"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "scale"
+    type: DT_FLOAT
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    default_value {
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+    }
+    allowed_values {
+      list {
+        type: DT_HALF
+        type: DT_FLOAT
+      }
+    }
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+}
+op {
+  name: "All"
+  input_arg {
+    name: "input"
+    type: DT_BOOL
+  }
+  input_arg {
+    name: "reduction_indices"
+    type_attr: "Tidx"
+  }
+  output_arg {
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+    type: DT_BOOL
+  }
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+    type: "list(type)"
+    has_minimum: true
+    minimum: 1
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+  attr {
+    name: "Tcaptured"
+    type: "list(type)"
+    has_minimum: true
+  }
+  attr {
+    name: "Tout"
+    type: "list(type)"
+    has_minimum: true
+    minimum: 1
+  }
+  attr {
+    name: "enable_large_batch_splitting"
+    type: "bool"
+    default_value {
+      b: false
+    }
+  }
+}
+op {
+  name: "BatchIFFT"
+  input_arg {
+    name: "input"
+    type: DT_COMPLEX64
+  }
+  output_arg {
+    name: "output"
+    type: DT_COMPLEX64
+  }
+  deprecation {
+    version: 15
+    explanation: "Use IFFT"
+  }
+}
+op {
+  name: "BatchIFFT2D"
+  input_arg {
+    name: "input"
+    type: DT_COMPLEX64
+  }
+  output_arg {
+    name: "output"
+    type: DT_COMPLEX64
+  }
+  deprecation {
+    version: 15
+    explanation: "Use IFFT2D"
+  }
+}
+op {
+  name: "BatchIFFT3D"
+  input_arg {
+    name: "input"
+    type: DT_COMPLEX64
+  }
+  output_arg {
+    name: "output"
+    type: DT_COMPLEX64
+  }
+  deprecation {
+    version: 15
+    explanation: "Use IFFT3D"
+  }
+}
+op {
+  name: "BatchMatMul"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_BFLOAT16
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+  attr {
+    name: "adj_x"
+    type: "bool"
+    default_value {
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+    }
+  }
+  attr {
+    name: "adj_y"
+    type: "bool"
+    default_value {
+      b: false
+    }
+  }
+}
+op {
+  name: "BatchMatMulV2"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_BFLOAT16
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+  attr {
+    name: "adj_x"
+    type: "bool"
+    default_value {
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+    }
+  }
+  attr {
+    name: "adj_y"
+    type: "bool"
+    default_value {
+      b: false
+    }
+  }
+}
+op {
+  name: "BatchMatrixBandPart"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "num_lower"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "num_upper"
+    type: DT_INT64
+  }
+  output_arg {
+    name: "band"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  deprecation {
+    version: 14
+    explanation: "Use MatrixBandPart"
+  }
+}
+op {
+  name: "BatchMatrixDeterminant"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+  deprecation {
+    version: 13
+    explanation: "Use MatrixDeterminant instead."
+  }
+}
+op {
+  name: "BatchMatrixDiag"
+  input_arg {
+    name: "diagonal"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  deprecation {
+    version: 14
+    explanation: "Use MatrixDiag"
+  }
+}
+op {
+  name: "BatchMatrixDiagPart"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "diagonal"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  deprecation {
+    version: 14
+    explanation: "Use MatrixDiagPart"
+  }
+}
+op {
+  name: "BatchMatrixInverse"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "adjoint"
+    type: "bool"
+    default_value {
+      b: false
+    }
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_DOUBLE
+        type: DT_FLOAT
+      }
+    }
+  }
+  deprecation {
+    version: 13
+    explanation: "Use MatrixInverse instead."
+  }
+}
+op {
+  name: "BatchMatrixSetDiag"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "diagonal"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  deprecation {
+    version: 14
+    explanation: "Use MatrixSetDiag"
+  }
+}
+op {
+  name: "BatchMatrixSolve"
+  input_arg {
+    name: "matrix"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "rhs"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
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+  attr {
+    name: "adjoint"
+    type: "bool"
+    default_value {
+      b: false
+    }
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_DOUBLE
+        type: DT_FLOAT
+      }
+    }
+  }
+  deprecation {
+    version: 13
+    explanation: "Use MatrixSolve instead."
+  }
+}
+op {
+  name: "BatchMatrixSolveLs"
+  input_arg {
+    name: "matrix"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "rhs"
+    type_attr: "T"
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+  input_arg {
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+    type: DT_DOUBLE
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+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_DOUBLE
+        type: DT_FLOAT
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+  attr {
+    name: "fast"
+    type: "bool"
+    default_value {
+      b: true
+    }
+  }
+  deprecation {
+    version: 13
+    explanation: "Use MatrixSolveLs instead."
+  }
+}
+op {
+  name: "BatchMatrixTriangularSolve"
+  input_arg {
+    name: "matrix"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "rhs"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
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+  attr {
+    name: "lower"
+    type: "bool"
+    default_value {
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+  attr {
+    name: "adjoint"
+    type: "bool"
+    default_value {
+      b: false
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+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_DOUBLE
+        type: DT_FLOAT
+      }
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+  }
+  deprecation {
+    version: 13
+    explanation: "Use MatrixTriangularSolve instead."
+  }
+}
+op {
+  name: "BatchNormWithGlobalNormalization"
+  input_arg {
+    name: "t"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "m"
+    type_attr: "T"
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+  input_arg {
+    name: "v"
+    type_attr: "T"
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+  input_arg {
+    name: "beta"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "gamma"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "result"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_INT64
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_BFLOAT16
+        type: DT_UINT16
+        type: DT_COMPLEX128
+        type: DT_HALF
+        type: DT_UINT32
+        type: DT_UINT64
+      }
+    }
+  }
+  attr {
+    name: "variance_epsilon"
+    type: "float"
+  }
+  attr {
+    name: "scale_after_normalization"
+    type: "bool"
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+  deprecation {
+    version: 9
+    explanation: "Use tf.nn.batch_normalization()"
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+}
+op {
+  name: "BatchNormWithGlobalNormalizationGrad"
+  input_arg {
+    name: "t"
+    type_attr: "T"
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+  input_arg {
+    name: "m"
+    type_attr: "T"
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+  input_arg {
+    name: "v"
+    type_attr: "T"
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+  input_arg {
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+    type_attr: "T"
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+  input_arg {
+    name: "backprop"
+    type_attr: "T"
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+  output_arg {
+    name: "dx"
+    type_attr: "T"
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+  output_arg {
+    name: "dm"
+    type_attr: "T"
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+  output_arg {
+    name: "dv"
+    type_attr: "T"
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+  output_arg {
+    name: "db"
+    type_attr: "T"
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+  output_arg {
+    name: "dg"
+    type_attr: "T"
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+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_INT64
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_BFLOAT16
+        type: DT_UINT16
+        type: DT_COMPLEX128
+        type: DT_HALF
+        type: DT_UINT32
+        type: DT_UINT64
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+  attr {
+    name: "variance_epsilon"
+    type: "float"
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+  attr {
+    name: "scale_after_normalization"
+    type: "bool"
+  }
+  deprecation {
+    version: 9
+    explanation: "Use tf.nn.batch_normalization()"
+  }
+}
+op {
+  name: "BatchSelfAdjointEig"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_DOUBLE
+        type: DT_FLOAT
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+  deprecation {
+    version: 11
+    explanation: "Use SelfAdjointEigV2 instead."
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+}
+op {
+  name: "BatchSelfAdjointEigV2"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "e"
+    type_attr: "T"
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+  output_arg {
+    name: "v"
+    type_attr: "T"
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+  attr {
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+  attr {
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+    type: "type"
+    allowed_values {
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+        type: DT_FLOAT
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+  deprecation {
+    version: 13
+    explanation: "Use SelfAdjointEigV2 instead."
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+}
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+  name: "BatchSvd"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "s"
+    type_attr: "T"
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+  output_arg {
+    name: "u"
+    type_attr: "T"
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+  output_arg {
+    name: "v"
+    type_attr: "T"
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+  attr {
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+  attr {
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+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
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+        type: DT_FLOAT
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
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+  deprecation {
+    version: 13
+    explanation: "Use Svd instead."
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+}
+op {
+  name: "BatchToSpace"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "crops"
+    type_attr: "Tidx"
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+  output_arg {
+    name: "output"
+    type_attr: "T"
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+  attr {
+    name: "T"
+    type: "type"
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+  attr {
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+    type: "int"
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+    minimum: 2
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+  attr {
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+    type: "type"
+    default_value {
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+    allowed_values {
+      list {
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+        type: DT_INT64
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+}
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+  name: "BatchToSpaceND"
+  input_arg {
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+    type_attr: "T"
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+  input_arg {
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+    type_attr: "Tblock_shape"
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+  input_arg {
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+    type_attr: "Tcrops"
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+  output_arg {
+    name: "output"
+    type_attr: "T"
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+  attr {
+    name: "T"
+    type: "type"
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+  attr {
+    name: "Tblock_shape"
+    type: "type"
+    default_value {
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+    allowed_values {
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+        type: DT_INT64
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+  attr {
+    name: "Tcrops"
+    type: "type"
+    default_value {
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+    allowed_values {
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+        type: DT_INT32
+        type: DT_INT64
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+}
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+  input_arg {
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+    type_attr: "T"
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+  output_arg {
+    name: "y"
+    type_attr: "T"
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+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
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+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
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+}
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+  name: "BesselI0e"
+  input_arg {
+    name: "x"
+    type_attr: "T"
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+  output_arg {
+    name: "y"
+    type_attr: "T"
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+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
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+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
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+}
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+  input_arg {
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+    type_attr: "T"
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+  output_arg {
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+    type_attr: "T"
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+  attr {
+    name: "T"
+    type: "type"
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+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
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+}
+op {
+  name: "BesselI1e"
+  input_arg {
+    name: "x"
+    type_attr: "T"
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+  output_arg {
+    name: "y"
+    type_attr: "T"
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+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
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+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
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+}
+op {
+  name: "BesselJ0"
+  input_arg {
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+    type_attr: "T"
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+  output_arg {
+    name: "y"
+    type_attr: "T"
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+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
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+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
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+}
+op {
+  name: "BesselJ1"
+  input_arg {
+    name: "x"
+    type_attr: "T"
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+  output_arg {
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+    type_attr: "T"
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+  attr {
+    name: "T"
+    type: "type"
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+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
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+}
+op {
+  name: "BesselK0"
+  input_arg {
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+    type_attr: "T"
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+  output_arg {
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+    type_attr: "T"
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+  attr {
+    name: "T"
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+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
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+}
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+  input_arg {
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+    type_attr: "T"
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+  output_arg {
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+    type_attr: "T"
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+  attr {
+    name: "T"
+    type: "type"
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+        type: DT_HALF
+        type: DT_FLOAT
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+}
+op {
+  name: "BesselK1"
+  input_arg {
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+    type_attr: "T"
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+  output_arg {
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+    type_attr: "T"
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+  attr {
+    name: "T"
+    type: "type"
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+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
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+}
+op {
+  name: "BesselK1e"
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+    type_attr: "T"
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+  output_arg {
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+    type_attr: "T"
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+  attr {
+    name: "T"
+    type: "type"
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+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
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+}
+op {
+  name: "BesselY0"
+  input_arg {
+    name: "x"
+    type_attr: "T"
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+  output_arg {
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+    type_attr: "T"
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+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
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+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
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+}
+op {
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+        type: DT_HALF
+      }
+    }
+  }
+  is_stateful: true
+}
+op {
+  name: "WriteRawProtoSummary"
+  input_arg {
+    name: "writer"
+    type: DT_RESOURCE
+  }
+  input_arg {
+    name: "step"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "tensor"
+    type: DT_STRING
+  }
+  is_stateful: true
+}
+op {
+  name: "WriteScalarSummary"
+  input_arg {
+    name: "writer"
+    type: DT_RESOURCE
+  }
+  input_arg {
+    name: "step"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "tag"
+    type: DT_STRING
+  }
+  input_arg {
+    name: "value"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_INT64
+        type: DT_BFLOAT16
+        type: DT_UINT16
+        type: DT_HALF
+        type: DT_UINT32
+        type: DT_UINT64
+      }
+    }
+  }
+  is_stateful: true
+}
+op {
+  name: "WriteSummary"
+  input_arg {
+    name: "writer"
+    type: DT_RESOURCE
+  }
+  input_arg {
+    name: "step"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "tensor"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "tag"
+    type: DT_STRING
+  }
+  input_arg {
+    name: "summary_metadata"
+    type: DT_STRING
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  is_stateful: true
+}
+op {
+  name: "Xdivy"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "z"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+}
+op {
+  name: "XlaBroadcastHelper"
+  input_arg {
+    name: "lhs"
+    description: "the LHS input tensor"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "rhs"
+    description: "the RHS input tensor"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "broadcast_dims"
+    description: "an XLA-style broadcast dimension specification"
+    type_attr: "Tindices"
+  }
+  output_arg {
+    name: "lhs_output"
+    description: "the broadcasted LHS tensor"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "rhs_output"
+    description: "the broadcasted RHS tensor"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_INT64
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_BFLOAT16
+        type: DT_UINT16
+        type: DT_COMPLEX128
+        type: DT_HALF
+        type: DT_UINT32
+        type: DT_UINT64
+      }
+    }
+  }
+  attr {
+    name: "Tindices"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  summary: "Helper operator for performing XLA-style broadcasts"
+  description: "Broadcasts `lhs` and `rhs` to the same rank, by adding size 1 dimensions to\nwhichever of `lhs` and `rhs` has the lower rank, using XLA\'s broadcasting rules\nfor binary operators."
+}
+op {
+  name: "XlaClusterOutput"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "outputs"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  summary: "Operator that connects the output of an XLA computation to other consumer graph nodes."
+}
+op {
+  name: "XlaConv"
+  input_arg {
+    name: "lhs"
+    description: "the input tensor"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "rhs"
+    description: "the kernel tensor"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "window_strides"
+    description: "the inter-window strides"
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "padding"
+    description: "the padding to apply at the start and end of each input dimensions"
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "lhs_dilation"
+    description: "dilation to apply between input elements"
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "rhs_dilation"
+    description: "dilation to apply between kernel elements"
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "feature_group_count"
+    description: "number of feature groups for grouped convolution."
+    type_attr: "Tindices"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_INT64
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_BFLOAT16
+        type: DT_UINT16
+        type: DT_COMPLEX128
+        type: DT_HALF
+        type: DT_UINT32
+        type: DT_UINT64
+      }
+    }
+  }
+  attr {
+    name: "Tindices"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  attr {
+    name: "dimension_numbers"
+    type: "string"
+    description: "a serialized xla::ConvolutionDimensionNumbers proto."
+  }
+  attr {
+    name: "precision_config"
+    type: "string"
+    description: "a serialized xla::PrecisionConfig proto."
+  }
+  summary: "Wraps the XLA ConvGeneralDilated operator, documented at"
+  description: " https://www.tensorflow.org/performance/xla/operation_semantics#conv_convolution\n."
+}
+op {
+  name: "XlaDequantize"
+  input_arg {
+    name: "input"
+    description: "Input tensors whose types is uint32, shape is [d0, ..., dn]."
+    type: DT_UINT32
+  }
+  output_arg {
+    name: "output"
+    description: "Output tensors whose types is bloat16. If transpose_output is true,\noutput shape is [dn * 4, dn-1, ..., d1, d0]. If transpose_output\nis false, output shape is [d0,..., dn * 4]."
+    type: DT_BFLOAT16
+  }
+  attr {
+    name: "min_range"
+    type: "float"
+    description: "The minimum scalar value possibly produced for the input."
+  }
+  attr {
+    name: "max_range"
+    type: "float"
+    description: "The maximum scalar value possibly produced for the input."
+  }
+  attr {
+    name: "mode"
+    type: "string"
+    description: "String to determine the dequantize mode in {\"MIN_COMBINED\", \"MIN_FIRST\", \"SCALED\"}."
+  }
+  attr {
+    name: "transpose_output"
+    type: "bool"
+    description: "Boolean to determine if output is transposed. transpose_output\nis faster when input is large and rank of input is higher than 1."
+  }
+  summary: "Takes the packed uint32 input and unpacks the input to uint8 to do"
+  description: "Dequantization on device."
+  is_stateful: true
+}
+op {
+  name: "XlaDot"
+  input_arg {
+    name: "lhs"
+    description: "the LHS tensor"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "rhs"
+    description: "the RHS tensor"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_INT64
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_BFLOAT16
+        type: DT_UINT16
+        type: DT_COMPLEX128
+        type: DT_HALF
+        type: DT_UINT32
+        type: DT_UINT64
+      }
+    }
+  }
+  attr {
+    name: "dimension_numbers"
+    type: "string"
+    description: "a serialized xla::DotDimensionNumbers proto."
+  }
+  attr {
+    name: "precision_config"
+    type: "string"
+    description: "a serialized xla::PrecisionConfig proto."
+  }
+  summary: "Wraps the XLA DotGeneral operator, documented at"
+  description: " https://www.tensorflow.org/performance/xla/operation_semantics#dotgeneral\n."
+}
+op {
+  name: "XlaDynamicSlice"
+  input_arg {
+    name: "input"
+    description: "A `Tensor` of type T."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "start_indices"
+    description: "List of N integers containing the slice size for each\ndimension. Each value must be strictly greater than zero, and start + size\nmust be less than or equal to the size of the dimension to avoid\nimplementation defined behavior."
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "size_indices"
+    type_attr: "Tindices"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  attr {
+    name: "Tindices"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  summary: "Wraps the XLA DynamicSlice operator, documented at"
+  description: " https://www.tensorflow.org/performance/xla/operation_semantics#dynamicslice\n.\n\nDynamicSlice extracts a sub-array from the input array at dynamic\nstart_indices. The size of the slice in each dimension is passed in\nsize_indices, which specify the end point of exclusive slice intervals in each\ndimension -- [start, start + size). The shape of start_indices must have rank 1,\nwith dimension size equal to the rank of operand."
+}
+op {
+  name: "XlaDynamicUpdateSlice"
+  input_arg {
+    name: "input"
+    description: "A `Tensor` of type T."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "update"
+    description: "A `Tensor` of type T. Same rank as `input`."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "indices"
+    description: "A vector of indices into `input`. Must have length equal to the rank of\n`input`."
+    type_attr: "Tindices"
+  }
+  output_arg {
+    name: "output"
+    description: "A `Tensor` of type T."
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  attr {
+    name: "Tindices"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  summary: "Wraps the XLA DynamicUpdateSlice operator, documented at"
+  description: " https://www.tensorflow.org/performance/xla/operation_semantics#dynamicupdateslice\n.\n\nXlaDynamicUpdateSlice generates a result which is the value of the `input`\noperand, with a slice update overwritten at `indices`. The shape of `update`\ndetermines the shape of the sub-array of the result which is updated. The shape\nof indices must be rank == 1, with dimension size equal to the rank of `input`.\n\nHandling of out-of-bounds slice indices is implementation-defined."
+}
+op {
+  name: "XlaEinsum"
+  input_arg {
+    name: "a"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "b"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "product"
+    type_attr: "T"
+  }
+  attr {
+    name: "equation"
+    type: "string"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_COMPLEX64
+        type: DT_BFLOAT16
+        type: DT_FLOAT
+      }
+    }
+  }
+  summary: "An op which supports basic einsum op with 2 inputs and 1 output."
+  description: "This op has better TPU performance since it doesn\'t have explicitly reshape and\ntranspose operations as tf.einsum does."
+}
+op {
+  name: "XlaGather"
+  input_arg {
+    name: "operand"
+    description: "The array we\'re gathering from."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "start_indices"
+    description: "Array containing the starting indices of the slices we gather."
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "slice_sizes"
+    description: "slice_sizes[i] is the bounds for the slice on dimension i."
+    type_attr: "Tindices"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "dimension_numbers"
+    type: "string"
+    description: "A serialized xla::GatherDimensionNumbers proto."
+  }
+  attr {
+    name: "indices_are_sorted"
+    type: "bool"
+    description: "Boolean indicating if the indices are sorted."
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_INT64
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_BFLOAT16
+        type: DT_UINT16
+        type: DT_COMPLEX128
+        type: DT_HALF
+        type: DT_UINT32
+        type: DT_UINT64
+        type: DT_BOOL
+      }
+    }
+  }
+  attr {
+    name: "Tindices"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  summary: "Wraps the XLA Gather operator documented at"
+  description: "  https://www.tensorflow.org/xla/operation_semantics#gather"
+}
+op {
+  name: "XlaHostCompute"
+  input_arg {
+    name: "inputs"
+    type_list_attr: "Tinputs"
+  }
+  output_arg {
+    name: "outputs"
+    type_list_attr: "Toutputs"
+  }
+  attr {
+    name: "Tinputs"
+    type: "list(type)"
+    has_minimum: true
+  }
+  attr {
+    name: "Toutputs"
+    type: "list(type)"
+    has_minimum: true
+  }
+  attr {
+    name: "ancestors"
+    type: "list(string)"
+    has_minimum: true
+  }
+  attr {
+    name: "shapes"
+    type: "list(shape)"
+    has_minimum: true
+  }
+  attr {
+    name: "shape_inference_graph"
+    type: "func"
+  }
+  attr {
+    name: "key"
+    type: "string"
+  }
+  attr {
+    name: "cost_estimate_ns"
+    type: "int"
+    default_value {
+      i: 1000000
+    }
+  }
+  attr {
+    name: "tpu_core"
+    type: "int"
+    default_value {
+      i: 0
+    }
+  }
+  is_stateful: true
+}
+op {
+  name: "XlaIf"
+  input_arg {
+    name: "cond"
+    description: "A boolean scalar."
+    type_attr: "Tcond"
+  }
+  input_arg {
+    name: "inputs"
+    description: "A list of input tensors."
+    type_list_attr: "Tin"
+  }
+  output_arg {
+    name: "output"
+    description: "A list of tensors returned by either then_branch(inputs) or\nelse_branch(inputs). The input shapes of the then_branch and\nelse_branch must match."
+    type_list_attr: "Tout"
+  }
+  attr {
+    name: "Tcond"
+    type: "type"
+  }
+  attr {
+    name: "then_branch"
+    type: "func"
+    description: "A function takes \'inputs\' and returns a list of tensors,\nwhose types are the same as what else_branch returns."
+  }
+  attr {
+    name: "else_branch"
+    type: "func"
+    description: "A function takes \'inputs\' and returns a list of tensors.\nwhose types are the same as what then_branch returns."
+  }
+  attr {
+    name: "Tin"
+    type: "list(type)"
+    has_minimum: true
+  }
+  attr {
+    name: "Tout"
+    type: "list(type)"
+    has_minimum: true
+  }
+  summary: "output = cond ? then_branch(inputs) : else_branch(inputs)."
+  is_stateful: true
+}
+op {
+  name: "XlaKeyValueSort"
+  input_arg {
+    name: "keys"
+    description: "A `Tensor` of type K."
+    type_attr: "K"
+  }
+  input_arg {
+    name: "values"
+    description: "A `Tensor` of type V."
+    type_attr: "V"
+  }
+  output_arg {
+    name: "sorted_keys"
+    description: "A `Tensor` of type K."
+    type_attr: "K"
+  }
+  output_arg {
+    name: "sorted_values"
+    description: "A `Tensor` of type V."
+    type_attr: "V"
+  }
+  attr {
+    name: "K"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_INT64
+        type: DT_BFLOAT16
+        type: DT_UINT16
+        type: DT_HALF
+        type: DT_UINT32
+        type: DT_UINT64
+      }
+    }
+  }
+  attr {
+    name: "V"
+    type: "type"
+  }
+  summary: "Wraps the XLA Sort operator, documented at"
+  description: " https://www.tensorflow.org/performance/xla/operation_semantics#sort\n.\n\nSorts a tensor. Currently only sorts in ascending order are supported."
+}
+op {
+  name: "XlaLaunch"
+  input_arg {
+    name: "constants"
+    type_list_attr: "Tconstants"
+  }
+  input_arg {
+    name: "args"
+    type_list_attr: "Targs"
+  }
+  input_arg {
+    name: "resources"
+    type: DT_RESOURCE
+    number_attr: "Nresources"
+  }
+  output_arg {
+    name: "results"
+    type_list_attr: "Tresults"
+  }
+  attr {
+    name: "Tconstants"
+    type: "list(type)"
+    has_minimum: true
+  }
+  attr {
+    name: "Targs"
+    type: "list(type)"
+    has_minimum: true
+  }
+  attr {
+    name: "Nresources"
+    type: "int"
+    has_minimum: true
+  }
+  attr {
+    name: "Tresults"
+    type: "list(type)"
+    has_minimum: true
+  }
+  attr {
+    name: "function"
+    type: "func"
+  }
+  summary: "XLA Launch Op. For use by the XLA JIT only."
+  is_stateful: true
+}
+op {
+  name: "XlaPad"
+  input_arg {
+    name: "input"
+    description: "A `Tensor` of type T."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "padding_value"
+    description: "A scalar `Tensor` of type T."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "padding_low"
+    description: "the padding to apply at the start of each input dimensions"
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "padding_high"
+    description: "the padding to apply at the end of each input dimension."
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "padding_interior"
+    description: "the padding to apply between each input element."
+    type_attr: "Tindices"
+  }
+  output_arg {
+    name: "output"
+    description: "A `Tensor` of type T."
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  attr {
+    name: "Tindices"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  summary: "Wraps the XLA Pad operator, documented at"
+  description: " https://www.tensorflow.org/performance/xla/operation_semantics#pad\n."
+}
+op {
+  name: "XlaRecv"
+  output_arg {
+    name: "tensor"
+    description: "The tensor to receive."
+    type_attr: "dtype"
+  }
+  attr {
+    name: "dtype"
+    type: "type"
+    description: "The type of the tensor."
+  }
+  attr {
+    name: "tensor_name"
+    type: "string"
+    description: "A string key that identifies the channel."
+  }
+  attr {
+    name: "shape"
+    type: "shape"
+    description: "The shape of the tensor."
+  }
+  summary: "Receives the named tensor from another XLA computation. Wraps the XLA Recv"
+  description: "operator documented at\n https://www.tensorflow.org/performance/xla/operation_semantics#recv ."
+  is_stateful: true
+}
+op {
+  name: "XlaRecvFromHost"
+  output_arg {
+    name: "output"
+    type_attr: "Toutput"
+  }
+  attr {
+    name: "Toutput"
+    type: "type"
+  }
+  attr {
+    name: "shape"
+    type: "shape"
+  }
+  attr {
+    name: "key"
+    type: "string"
+  }
+  is_stateful: true
+}
+op {
+  name: "XlaReduce"
+  input_arg {
+    name: "input"
+    description: "the input tensor"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "init_value"
+    description: "a scalar representing the initial value for the reduction"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_INT64
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_BFLOAT16
+        type: DT_UINT16
+        type: DT_COMPLEX128
+        type: DT_HALF
+        type: DT_UINT32
+        type: DT_UINT64
+      }
+    }
+  }
+  attr {
+    name: "dimensions_to_reduce"
+    type: "list(int)"
+    description: "dimension numbers over which to reduce"
+  }
+  attr {
+    name: "reducer"
+    type: "func"
+    description: "a reducer function to apply"
+  }
+  summary: "Wraps the XLA Reduce operator, documented at"
+  description: " https://www.tensorflow.org/performance/xla/operation_semantics#reduce ."
+}
+op {
+  name: "XlaReduceWindow"
+  input_arg {
+    name: "input"
+    description: "the input tensor"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "init_value"
+    description: "a scalar representing the initial value for the reduction"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "window_dimensions"
+    description: "the shape of the window"
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "window_strides"
+    description: "the inter-window strides"
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "base_dilations"
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "window_dilations"
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "padding"
+    description: "the padding to apply at the start and end of each input dimensions"
+    type_attr: "Tindices"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_INT64
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_BFLOAT16
+        type: DT_UINT16
+        type: DT_COMPLEX128
+        type: DT_HALF
+        type: DT_UINT32
+        type: DT_UINT64
+      }
+    }
+  }
+  attr {
+    name: "Tindices"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  attr {
+    name: "computation"
+    type: "func"
+    description: "a reducer function to apply"
+  }
+  summary: "Wraps the XLA ReduceWindow operator, documented at"
+  description: " https://www.tensorflow.org/performance/xla/operation_semantics#reducewindow ."
+}
+op {
+  name: "XlaReplicaId"
+  output_arg {
+    name: "id"
+    type: DT_INT32
+  }
+  summary: "Replica ID."
+}
+op {
+  name: "XlaScatter"
+  input_arg {
+    name: "operand"
+    description: "Array to be scattered into."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "scatter_indices"
+    description: "Array containing the starting indices of the slices that must\nbe scattered to."
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "updates"
+    description: "Array containing the values that must be used for scattering."
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "update_computation"
+    type: "func"
+    description: "Computation to be used for combining the existing values in\nthe input array and the updates during scatter."
+  }
+  attr {
+    name: "dimension_numbers"
+    type: "string"
+    description: "A serialized xla::ScatterDimensionNumbers proto."
+  }
+  attr {
+    name: "indices_are_sorted"
+    type: "bool"
+    description: "Boolean indicating if the indices are sorted."
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_INT64
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_BFLOAT16
+        type: DT_UINT16
+        type: DT_COMPLEX128
+        type: DT_HALF
+        type: DT_UINT32
+        type: DT_UINT64
+        type: DT_BOOL
+      }
+    }
+  }
+  attr {
+    name: "Tindices"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  summary: "Wraps the XLA Scatter operator documented at"
+  description: "  https://www.tensorflow.org/xla/operation_semantics#scatter."
+}
+op {
+  name: "XlaSelectAndScatter"
+  input_arg {
+    name: "operand"
+    description: "the input tensor"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "window_dimensions"
+    description: "the shape of the window"
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "window_strides"
+    description: "the inter-window strides"
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "padding"
+    description: "the padding to apply at the start and end of each input dimensions"
+    type_attr: "Tindices"
+  }
+  input_arg {
+    name: "source"
+    description: "a tensor of values to scatter"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "init_value"
+    description: "a scalar representing the initial value for the output tensor"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_INT64
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_BFLOAT16
+        type: DT_UINT16
+        type: DT_COMPLEX128
+        type: DT_HALF
+        type: DT_UINT32
+        type: DT_UINT64
+      }
+    }
+  }
+  attr {
+    name: "Tindices"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  attr {
+    name: "select"
+    type: "func"
+    description: "a selection function to apply"
+  }
+  attr {
+    name: "scatter"
+    type: "func"
+    description: "a scatter function to apply"
+  }
+  summary: "Wraps the XLA SelectAndScatter operator, documented at"
+  description: " https://www.tensorflow.org/performance/xla/operation_semantics#selectandscatter\n."
+}
+op {
+  name: "XlaSelfAdjointEig"
+  input_arg {
+    name: "a"
+    description: "the input tensor."
+    type_attr: "T"
+  }
+  output_arg {
+    name: "w"
+    description: "The eigenvalues in ascending order, each repeated according to its\nmultiplicity."
+    type_attr: "T"
+  }
+  output_arg {
+    name: "v"
+    description: "The column v[..., :, i] is the normalized eigenvector corresponding to the\neigenvalue w[..., i]."
+    type_attr: "T"
+  }
+  attr {
+    name: "lower"
+    type: "bool"
+    description: "a boolean specifies whether the calculation is done with the lower\ntriangular part or the upper triangular part."
+  }
+  attr {
+    name: "max_iter"
+    type: "int"
+    description: "maximum number of sweep update, i.e., the whole lower triangular\npart or upper triangular part based on parameter lower. Heuristically, it has\nbeen argued that approximately logN sweeps are needed in practice (Ref: Golub &\nvan Loan \"Matrix Computation\")."
+  }
+  attr {
+    name: "epsilon"
+    type: "float"
+    description: "the tolerance ratio."
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_INT64
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_BFLOAT16
+        type: DT_UINT16
+        type: DT_COMPLEX128
+        type: DT_HALF
+        type: DT_UINT32
+        type: DT_UINT64
+      }
+    }
+  }
+  summary: "Computes the eigen decomposition of a batch of self-adjoint matrices"
+  description: "(Note: Only real inputs are supported).\n\nComputes the eigenvalues and eigenvectors of the innermost N-by-N matrices in\ntensor such that tensor[...,:,:] * v[..., :,i] = e[..., i] * v[...,:,i], for\ni=0...N-1."
+}
+op {
+  name: "XlaSend"
+  input_arg {
+    name: "tensor"
+    description: "The tensor to send."
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  attr {
+    name: "tensor_name"
+    type: "string"
+    description: "A string key that identifies the channel."
+  }
+  summary: "Sends the named tensor to another XLA computation. Wraps the XLA Send operator"
+  description: "documented at\n https://www.tensorflow.org/performance/xla/operation_semantics#send ."
+  is_stateful: true
+}
+op {
+  name: "XlaSendToHost"
+  input_arg {
+    name: "input"
+    type_attr: "Tinput"
+  }
+  attr {
+    name: "Tinput"
+    type: "type"
+  }
+  attr {
+    name: "key"
+    type: "string"
+  }
+  is_stateful: true
+}
+op {
+  name: "XlaSetBound"
+  input_arg {
+    name: "input"
+    type: DT_INT32
+  }
+  input_arg {
+    name: "bound"
+    type: DT_INT32
+  }
+  output_arg {
+    name: "output"
+    type: DT_INT32
+  }
+  summary: "Set a bound for the given input value as a hint to Xla compiler,"
+  description: "        returns the same value."
+}
+op {
+  name: "XlaSharding"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  summary: "An op which shards the input based on the given sharding attribute."
+}
+op {
+  name: "XlaSort"
+  input_arg {
+    name: "input"
+    description: "A `Tensor` of type T."
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    description: "A `Tensor` of type T."
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  summary: "Wraps the XLA Sort operator, documented at"
+  description: " https://www.tensorflow.org/performance/xla/operation_semantics#sort\n.\n\nSorts a tensor. Currently only sorts in ascending order are supported."
+}
+op {
+  name: "XlaSpmdFullToShardShape"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  attr {
+    name: "manual_sharding"
+    type: "string"
+  }
+  summary: "An op used by XLA SPMD partitioner to switch from automatic partitioning to"
+  description: "manual partitioning. It annotates the input (full-shape, to be automatically\npartitioned) with the same sharding used by manual partitioning, and outputs a\nshard-shaped tensor to be consumed by later manually-partitioned ops. If the\nshape is not evenly partitionable, the padding region will be masked with 0s."
+}
+op {
+  name: "XlaSpmdShardToFullShape"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  attr {
+    name: "manual_sharding"
+    type: "string"
+  }
+  attr {
+    name: "full_shape"
+    type: "shape"
+  }
+  summary: "An op used by XLA SPMD partitioner to switch from manual partitioning to"
+  description: "automatic partitioning. It converts the shard-shaped, manually partitioned input\ninto full-shaped tensor to be partitioned automatically with the same sharding\nused by manual partitioning."
+}
+op {
+  name: "XlaSvd"
+  input_arg {
+    name: "a"
+    description: "the input tensor."
+    type_attr: "T"
+  }
+  output_arg {
+    name: "s"
+    description: "Singular values. The values are sorted in reverse order of magnitude, so\ns[..., 0] is the largest value, s[..., 1] is the second largest, etc."
+    type_attr: "T"
+  }
+  output_arg {
+    name: "u"
+    description: "Left singular vectors."
+    type_attr: "T"
+  }
+  output_arg {
+    name: "v"
+    description: "Right singular vectors."
+    type_attr: "T"
+  }
+  attr {
+    name: "max_iter"
+    type: "int"
+    description: "maximum number of sweep update, i.e., the whole lower triangular\npart or upper triangular part based on parameter lower. Heuristically, it has\nbeen argued that approximately log(min (M, N)) sweeps are needed in practice\n(Ref: Golub & van Loan \"Matrix Computation\")."
+  }
+  attr {
+    name: "epsilon"
+    type: "float"
+    description: "the tolerance ratio."
+  }
+  attr {
+    name: "precision_config"
+    type: "string"
+    description: "a serialized xla::PrecisionConfig proto."
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_INT64
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_BFLOAT16
+        type: DT_UINT16
+        type: DT_COMPLEX128
+        type: DT_HALF
+        type: DT_UINT32
+        type: DT_UINT64
+      }
+    }
+  }
+  summary: "Computes the eigen decomposition of a batch of self-adjoint matrices"
+  description: "(Note: Only real inputs are supported).\n\nComputes the eigenvalues and eigenvectors of the innermost M-by-N matrices in\ntensor such that tensor[...,:,:] = u[..., :, :] * Diag(s[..., :]) * Transpose(v[...,:,:])."
+}
+op {
+  name: "XlaVariadicReduce"
+  input_arg {
+    name: "input"
+    description: "the input tensor(s)"
+    type_attr: "T"
+    number_attr: "N"
+  }
+  input_arg {
+    name: "init_value"
+    description: "scalar initial value(s) for the reduction"
+    type_attr: "T"
+    number_attr: "N"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+    number_attr: "N"
+  }
+  attr {
+    name: "N"
+    type: "int"
+    has_minimum: true
+    minimum: 1
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_INT64
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_BFLOAT16
+        type: DT_UINT16
+        type: DT_COMPLEX128
+        type: DT_HALF
+        type: DT_UINT32
+        type: DT_UINT64
+      }
+    }
+  }
+  attr {
+    name: "dimensions_to_reduce"
+    type: "list(int)"
+    description: "dimension numbers over which to reduce"
+  }
+  attr {
+    name: "reducer"
+    type: "func"
+    description: "a reducer function to apply"
+  }
+  summary: "Wraps the variadic XLA Reduce operator, documented at"
+  description: " https://www.tensorflow.org/performance/xla/operation_semantics#variadic_reduce."
+}
+op {
+  name: "XlaWhile"
+  input_arg {
+    name: "input"
+    description: "A list of input tensors whose types are T."
+    type_list_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    description: "A list of output tensors whose types are T."
+    type_list_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "list(type)"
+    has_minimum: true
+  }
+  attr {
+    name: "cond"
+    type: "func"
+    description: "A function takes \'input\' and returns a tensor.  If the tensor is\na scalar of non-boolean, the scalar is converted to a boolean\naccording to the following rule: if the scalar is a numerical\nvalue, non-zero means True and zero means False; if the scalar is\na string, non-empty means True and empty means False. If the\ntensor is not a scalar, non-emptiness means True and False\notherwise."
+  }
+  attr {
+    name: "body"
+    type: "func"
+    description: "A function that takes a list of tensors and returns another\nlist of tensors. Both lists have the same types as specified by T."
+  }
+  summary: "output = input; While (Cond(output)) { output = Body(output) }"
+  is_stateful: true
+}
+op {
+  name: "Xlog1py"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "z"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+}
+op {
+  name: "Xlogy"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "z"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+}
+op {
+  name: "ZerosLike"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+}
+op {
+  name: "Zeta"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "q"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "z"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+      }
+    }
+  }
+}
+op {
+  name: "ZipDataset"
+  input_arg {
+    name: "input_datasets"
+    type: DT_VARIANT
+    number_attr: "N"
+  }
+  output_arg {
+    name: "handle"
+    type: DT_VARIANT
+  }
+  attr {
+    name: "output_types"
+    type: "list(type)"
+    has_minimum: true
+    minimum: 1
+  }
+  attr {
+    name: "output_shapes"
+    type: "list(shape)"
+    has_minimum: true
+    minimum: 1
+  }
+  attr {
+    name: "N"
+    type: "int"
+    has_minimum: true
+    minimum: 1
+  }
+}
diff --git a/tensorflow-core/tensorflow-core-api/src/gen/resources/org/tensorflow/op/ops.pb b/tensorflow-core/tensorflow-core-api/src/gen/resources/org/tensorflow/op/ops.pb
deleted file mode 100644
index 5472f5f8839..00000000000
Binary files a/tensorflow-core/tensorflow-core-api/src/gen/resources/org/tensorflow/op/ops.pb and /dev/null differ