if (!status->status.ok()) {
    return nullptr;
  }
  return tensor;
}

// Deleter for DLManagedTensor
void DLManagedTensorDeleter(DLManagedTensor* arg) {
  TfDlManagedTensorCtx* owner =
      static_cast<TfDlManagedTensorCtx*>(arg->manager_ctx);
  owner->reference.Unref();
  delete owner;
}

// Converts TF_DATAType to DLPack data type.
DLDataType GetDlDataType(TF_DataType data_type, TF_Status* status) {

          CI_DOCKER_BUILD_EXTRA_PARAMS="--build-arg py_major_minor_version=${{ matrix.pyver }} --build-arg is_nightly=${is_nightly} --build-arg tf_project_name=${tf_project_name}" \

  // Verify that FunctionDef ArgDef has attributes.
  ASSERT_EQ(func_->record->fdef().arg_attr_size(), 1);
  auto arg_attrs = func_->record->fdef().arg_attr().find(0);
  ASSERT_NE(arg_attrs, func_->record->fdef().arg_attr().end());
  auto iter = arg_attrs->second.attr().find("_test_attr");
  ASSERT_NE(iter, arg_attrs->second.attr().end());
  EXPECT_EQ(iter->second.s(), "value");
}

    TFE_Context* ctx, const char* device_name, TF_DataType, const int64_t* dims,
    int num_dims, void* data, size_t len,
    void (*deallocator)(void* data, size_t len, void* arg),
    void* deallocator_arg, TF_Status* status);

// Retrieves the address space (i.e. job, replia, task) of the local host and
// saves it in the buffer.
TF_CAPI_EXPORT extern void TFE_HostAddressSpace(TFE_Context* ctx,

  // Tensor creation functions
  virtual AbstractTensorInterface* CreateTensor(
      DataType dtype, absl::Span<const int64_t> dim_sizes) = 0;

  typedef void (*MemoryReleaser)(void* data, size_t len, void* arg);

  // Create a tensor instance from the given data buffer and description.
  // `memory_releaser` will be called on destruction, and it's responsible for
  // cleaning up the underlying buffer.

*   <THIS SECTION SHOULD CONTAIN API, ABI AND BEHAVIORAL BREAKING CHANGES>

*   `tf.summary.trace_on` now takes a `profiler_outdir` argument. This must be
    set if `profiler` arg is set to `True`.

    *   `tf.summary.trace_export`'s `profiler_outdir` arg is now a no-op.
        Enabling the profiler now requires setting `profiler_outdir` in
        `trace_on`.

*   `tf.estimator`

    *   The tf.estimator API is removed.

    void (*deallocator)(void* data, size_t len, void* arg),
    void* deallocator_arg, TF_Status* status) {
  tensorflow::Device* device = nullptr;
  tensorflow::EagerContext* context =
      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
  status->status = context->FindDeviceFromName(device_name, &device);
  if (!status->status.ok()) {
    deallocator(data, len, deallocator_arg);
    status->status =

      - name: Build binary and run python tests
        shell: bash
        run: |
          CI_DOCKER_BUILD_EXTRA_PARAMS="--pull --build-arg py_major_minor_version=${{ matrix.pyver }} --build-arg is_nightly=1 --build-arg tf_project_name=tf_nightly_ci" \

  // First create a simple identity function.
  TF_Graph* function_graph = TF_NewGraph();
  TF_OperationDescription* arg_descr =
      TF_NewOperation(function_graph, "Placeholder", "arg");
  TF_SetAttrType(arg_descr, "dtype", TF_INT32);
  TF_Status* status = TF_NewStatus();
  TF_Operation* arg = TF_FinishOperation(arg_descr, status);
  ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status);
  TF_OperationDescription* id_descr =

    ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

    auto cpu_arg =
        tensorflow::TensorHandleFromInterface(tensorflow::unwrap(hcpu));
    auto gpu_arg =
        tensorflow::TensorHandleFromInterface(tensorflow::unwrap(hgpu));
    auto gpu_device = gpu_arg->device();
    ASSERT_FALSE(cpu_arg->HasLocalMirror(gpu_device));

    TFE_Op* matmul = MatMulOp(ctx, hcpu, hgpu);
    if (cpu_op) {