#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {

// Abstract interface to a TensorHandle.
//
// A TensorHandle is management class around a Tensor which may track additional
// metadata and synchronization.
//
// This allows us to hide concrete implementations of TensorHandle from header
// files. The interface lists the common functionality that must be provided by

limitations under the License.
==============================================================================*/

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

#include <vector>

#include "absl/strings/str_join.h"
#include "include/dlpack/dlpack.h"  // from @dlpack
#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace {

==============================================================================*/

#ifndef TENSORFLOW_C_EAGER_DLPACK_H_
#define TENSORFLOW_C_EAGER_DLPACK_H_

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

namespace tensorflow {

// PyCapsule name for DLPack Tensor
const char* const kDlTensorCapsuleName = "dltensor";

// Converts eager tensor handle to DLPack (DLManagedTensor*), and return the
// void* for further PyCapsule construction.

Michael Hudgins <******@****.***> 1695048765 +0000

// -----------------------------------------------------------------------------

// A TF_ExecutionContext stores knowledge about how to execute an operation.
// E.g. it could know whether we're in eager mode or graph mode, keeps track
// of gradient tapes, etc.
typedef struct TF_ExecutionContext TF_ExecutionContext;

// A TF_AbstractTensor is an input to an operation. E.g. it could be a union

tfrun bazel $TFCI_BAZEL_BAZELRC_ARGS test $TFCI_BAZEL_COMMON_ARGS --config=linux_libtensorflow_test
tfrun bazel $TFCI_BAZEL_BAZELRC_ARGS build $TFCI_BAZEL_COMMON_ARGS --config=linux_libtensorflow_build

tfrun ./ci/official/utilities/repack_libtensorflow.sh "$TFCI_OUTPUT_DIR" "$TFCI_LIB_SUFFIX"

if [[ "$TFCI_ARTIFACT_STAGING_GCS_ENABLE" == 1 ]]; then
  # Note: -n disables overwriting previously created files.

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
scenario would require a malicious user to be able to control the model which as
stated above is already equivalent to code execution. In any case, the
recommendation is not to serve models using Eager mode since it also has

  if (use_function) {
    const char* fn_name = "test_fn";
    core::RefCountPtr<AbstractFunction> scoped_func;
    // Returning null tensors from a tf.function is not supported, so we keep
    // track of indices in the model's outputs are nullptr in this set.
    // The FunctionDef only outputs the non-null tensors. We later pad the
    // function op outputs to have nullptrs at the `null_indices`.

TensorFlower Gardener <******@****.***> 1689191273 -0700

};

/** For trusted parters like Intel, we want to auto-run tests
    This allows us to reduce the delay to external partners
    Add Labels - kokoro:force-run
    The PR is also assigned to specific teams to fast track review
    Additional reviewers can be added manually based on PR contents
  @param {!object}
    github enables querying for PR and also create issue using rest endpoint
    context has the commit message details in the payload