Status InitGraphPlugin(void* dso_handle);

// Allow registering a graph optimizer using a function (used for
// testing).
Status InitGraphPlugin(TFInitGraphPluginFn init_fn);

struct GrapplerItem;
class Cluster;

struct TFStatusDeleter {
  void operator()(TF_Status* s) const { TF_DeleteStatus(s); }
};
using OwnedTFStatus = std::unique_ptr<TF_Status, TFStatusDeleter>;

struct TFBufferDeleter {

#include "tensorflow/c/tf_tensor.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {

// Builds and returns a `TracingContext` using the default tracing impl.
AbstractContext* BuildFunction(const char* fn_name);

// Creates parameters (placeholders) in the tracing `ctx` using the shape and
// dtype of `inputs`.
Status CreateParamsForInputs(AbstractContext* ctx,

                          remote_device) != device_names.end());
  }

  // Create a variable using `ctx_0`.
  // Replace worker1 using a new worker, and update the contexts.
  // Read the variable using `ctx_1`. This read should succeed.
  //
  // 1. Create a variable on `remote_device`, using `ctx_0`.
  TFE_TensorHandle* handle_0 =
      CreateVariable(ctx_0, 1.2, remote_device, /*variable_name=*/"var");

and a requirements_lock file. The requirements_lock file is generated by the
[requirements_updater](https://github.com/tensorflow/tensorflow/tree/master/ci/official/requirements_updater)
tool using the path to this wheel file.

### Hermetic Python

For details about hermetic Python and setting its toolchain version, see

# limitations under the License.
# ==============================================================================
# Sourcing this enables local disk cache
if [[ $(uname -s) == "Darwin" ]]; then
  echo "Please note that using disk cache on macOS is not recommended because the"
  echo "cache can end up being pretty big and make the build process inefficient."
fi

                          remote_device) != device_names.end());
  }

  // Create a variable using `ctx_0`.
  // Replace worker1 using a new worker, and update the contexts.
  // Read the variable using `ctx_1`. This read should succeed.
  //
  // 1. Create a variable on `remote_device`, using `ctx_0`.
  TFE_TensorHandle* handle_0 =
      CreateVariable(ctx_0, 1.2, remote_device, /*variable_name=*/"var");

#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 {

std::vector<int64_t> TensorShapeAsVector(const tensorflow::TensorHandle& handle,
                                         tensorflow::Status* status) {
  std::vector<int64_t> shape;

only address code/doc bugs, performance issues, feature requests and
build/installation issues on GitHub.

-   type: markdown
    attributes:
      value: |

namespace internal {
struct ImmediateExecutionTensorHandleDeleter {
  void operator()(ImmediateExecutionTensorHandle* p) const {
    if (p != nullptr) {
      p->Unref();
    }
  }
};
}  // namespace internal

using ImmediateTensorHandlePtr =
    std::unique_ptr<ImmediateExecutionTensorHandle,
                    internal::ImmediateExecutionTensorHandleDeleter>;

}  // namespace tensorflow

#include "tensorflow/core/platform/tensor_float_32_utils.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace gradients {
namespace internal {
namespace {

using tensorflow::TF_StatusPtr;

Status ReluModel(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> inputs,
                 absl::Span<AbstractTensorHandle*> outputs) {