#ifndef TENSORFLOW_C_EAGER_GRAPH_FUNCTION_H_
#define TENSORFLOW_C_EAGER_GRAPH_FUNCTION_H_

#include "tensorflow/c/eager/abstract_function.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/platform/refcount.h"
namespace tensorflow {
namespace tracing {
namespace graph {
using tensorflow::AbstractFunction;
// Thin wrapper around a FunctionDef.
class GraphFunction : public AbstractFunction {
 public:

WIP ML Build Docker container for ML repositories (Tensorflow, JAX and XLA).

This container branches off from
/tensorflow/tools/tf_sig_build_dockerfiles/. However, since
hermetic CUDA and hermetic Python is now available for Tensorflow, a lot of the
requirements installed on the original container can be removed to reduce the
footprint of the container and make it more reusable across different ML

#ifndef TENSORFLOW_C_EAGER_ABSTRACT_OP_ATTRS_H_
#define TENSORFLOW_C_EAGER_ABSTRACT_OP_ATTRS_H_

#include "absl/container/inlined_vector.h"
#include "tensorflow/core/framework/attr_value.pb.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {

// Attributes of an op.
class AbstractOpAttrs {
 protected:
  enum AbstractOpAttrsKind { kEager, kTfrt };

#include "tensorflow/c/eager/gradients_internal.h"
#include "tensorflow/c/eager/unified_api_testutil.h"
#include "tensorflow/c/experimental/gradients/array_grad.h"
#include "tensorflow/c/experimental/gradients/math_grad.h"
#include "tensorflow/c/experimental/gradients/not_differentiable.h"
#include "tensorflow/c/experimental/gradients/tape/tape_context.h"
#include "tensorflow/c/experimental/ops/array_ops.h"

#include <vector>

#include "tensorflow/c/c_api.h"
#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/tfe_tensor_debug_info_internal.h"
#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"
#include "tensorflow/c/tf_status_internal.h"
#include "tensorflow/core/common_runtime/eager/tensor_handle.h"
#include "tensorflow/core/platform/status.h"

using tensorflow::string;

namespace {

# Release 2.11.1

**Note**: TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Starting with TensorFlow 2.11, you will need to install TensorFlow in WSL2, or install tensorflow-cpu and, optionally, try the TensorFlow-DirectML-Plugin.

#include "tensorflow/c/eager/c_api_remote_test_util.h"

#include "absl/strings/str_cat.h"
#include "tensorflow/c/eager/c_api_internal.h"
#include "tensorflow/c/eager/c_api_test_util.h"
#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"
#include "tensorflow/core/common_runtime/eager/tensor_handle.h"
#include "tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h"

        You can collect some of this information using our [environment capture script](https://github.com/tensorflow/tensorflow/tree/master/tools/tf_env_collect.sh). You can also obtain the TensorFlow version with the following: <br> 1. TensorFlow 1.0: `python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)"` <br>2. TensorFlow 2.0: `python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"`

  - type: dropdown
    id: source

}
DIR=$1
TARBALL_SUFFIX=$2
mkdir -p "$DIR"
cp bazel-bin/tensorflow/tools/lib_package/libtensorflow.tar.gz "${DIR}/libtensorflow${TARBALL_SUFFIX}.tar.gz"
cp bazel-bin/tensorflow/tools/lib_package/libtensorflow_jni.tar.gz "${DIR}/libtensorflow_jni${TARBALL_SUFFIX}.tar.gz"
cp bazel-bin/tensorflow/java/libtensorflow.jar "${DIR}"
cp_normalized_srcjar bazel-bin/tensorflow/java/libtensorflow-src.jar "${DIR}/libtensorflow-src.jar"

#define TENSORFLOW_C_C_TEST_UTIL_H_

#include "tensorflow/c/c_api.h"

#include <vector>
#include "tensorflow/core/framework/attr_value.pb.h"
#include "tensorflow/core/framework/function.pb.h"
#include "tensorflow/core/framework/graph.pb.h"
#include "tensorflow/core/framework/node_def.pb.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/test.h"

using ::tensorflow::string;