--with-default-libstdcxx-abi=${LIBSTDCXX_ABI} \
      --with-gcc-major-version-only \
      --with-linker-hash-style="gnu" \
      && \
      make -j$(nproc) && \
      make install


# Create the devtoolset libstdc++ linkerscript that links dynamically against
# the system libstdc++ 4.4 and provides all other symbols statically.
# Note that the installation path for libstdc++ here is ${TARGET}/usr/lib64/

    test to timeout but it could be useful when you need to execute a single
    test or more in general your filtered/selected tests have a very low
    execution time and the sharding
    [could create an overhead on the test execution](https://github.com/bazelbuild/bazel/issues/2113#issuecomment-264054799).

2.  Using [Docker](https://www.docker.com) and TensorFlow's CI scripts.

    ```bash

// The context should be identical to that of the Tensor.
TF_CAPI_EXPORT TFE_TensorHandle* TFE_NewTensorHandleFromTensor(
    TFE_Context* ctx, TF_Tensor* t, TF_Status* status);

// Create a packed TensorHandle with the given list of TensorHandles.
// If `handles` are on the same device, assign the same device to the packed
// handle; if `handles` are on different deivces, assign a CompositeDevice to
// it.

    void (*delete_func)(void*), TF_Status* status);

// If the ResourceMgr provided by the `ctx` has a resource
// `plugin_resource_name`, returns it in `*result_plugin_resource`. Otherwise,
// invokes create_func to create the resource. `delete_func` is needed for
// ResourceMgr to clean up the resource. `status` will be set. If `status` is
// not OK, `*result_plugin_resource` will be set as nullptr.
//

  // Create a handle to wrap and manage a Tensor
  virtual ImmediateExecutionTensorHandle* CreateLocalHandle(
      AbstractTensorInterface* t) = 0;
  // Copy the handle to another device.
  virtual ImmediateExecutionTensorHandle* CopyTensorHandleToDevice(
      ImmediateExecutionTensorHandle* handle, const char* device_name,
      Status* status) = 0;

  // Create an operation to perform op execution

# under the root of srcjars jars created by bazel, rather than under
# the maven-style src/main/java subdirectory.
#
# Bazel manages annotation generated source as follows: First, it
# calls javac with options that create generated files under a
# bazel-out directory. Next, it archives the generated source files
# into a srcjar directly under the root. There doesn't appear to be a
# simple way to parameterize this from bazel, hence this helper to

TFE_MonitoringSampler0* TFE_MonitoringNewSampler0(
    const char* name, TFE_MonitoringBuckets* buckets, TF_Status* status,
    const char* description) {
  auto* result = new TFE_MonitoringSampler0(
      {name, buckets->create_buckets(), description});
  tsl::Set_TF_Status_from_Status(status, result->sampler->GetStatus());
  if (!result->sampler->GetStatus().ok()) {
    delete result;
    return nullptr;
  }
  return result;
}

inspected and debugged and it is intended to be used during the development
phase.

As part of the differences that make Eager mode easier to debug, the [shape
inference
functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface)
are skipped, and any checks implemented inside the shape inference code are not
executed.

The security impact of skipping those checks should be low, since the attack

  return th;
}

TFE_TensorHandle* TestVariable(TFE_Context* ctx, float value,
                               const tensorflow::string& device_name) {
  TF_Status* status = TF_NewStatus();
  // Create the variable handle.
  TFE_Op* op = TFE_NewOp(ctx, "VarHandleOp", status);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_OpSetAttrType(op, "dtype", TF_FLOAT);
  TFE_OpSetAttrShape(op, "shape", {}, 0, status);

      TFE_Op* op,
      const std::vector<absl::optional<std::vector<int64_t>>>& input_shapes_vec,
      const std::vector<TF_Tensor*>& input_tensors,
      const absl::optional<std::vector<int64_t>>& expected_shape) {
    // Create input_shapes.
    TF_ShapeAndTypeList* input_shapes =
        TF_NewShapeAndTypeList(input_shapes_vec.size());
    for (size_t i = 0; i < input_shapes_vec.size(); ++i) {
      const auto& input_shape = input_shapes_vec[i];