virtual ImmediateExecutionContext* GetContext() const = 0;

  // Following two methods are used to support custom device.
  // Return true if the inputs contain custom device tensor handle. It means
  // that the argument need to be handled by a custom device.
  virtual bool HasCustomDeviceInput() const = 0;

  virtual const tensorflow::OpDef* OpDef() const = 0;

  TestHandleFromDLPack(status, ctx, {4, 1, 2}, {2, 0, 1});
  TestHandleFromDLPack(status, ctx, {4, 3, 1}, {3, 1, 1});
  TestHandleFromDLPack(status, ctx, {4, 3, 1}, {3, 1, 0});
  // Test that empty tensors can have any strides.
  TestHandleFromDLPack(status, ctx, {4, 0, 2}, {0, 2, 1});
  TestHandleFromDLPack(status, ctx, {4, 0, 2}, {0, 1, 1});
  TestHandleFromDLPack(status, ctx, {4, 0, 2}, {0, 0, 1});

                                 ForwardOperation*);
absl::Status SetAttrTensor(AbstractOperation*, const char* attr_name,
                           AbstractTensorInterface* tensor, ForwardOperation*);
absl::Status SetAttrStringList(AbstractOperation*, const char* attr_name,
                               const void* const* values, const size_t* lengths,

    op_->node_builder.Attr(attr_name, func_name);
    return absl::OkStatus();
  }
  absl::Status SetAttrTensor(const char* attr_name,
                             AbstractTensorInterface* tensor) override {
    return tensorflow::errors::Unimplemented(
        "SetAttrTensor has not been implemented yet.");
  }
  absl::Status SetAttrStringList(const char* attr_name,

                                           size_t length) = 0;
  virtual absl::Status SetAttrTensor(const char* attr_name,
                                     AbstractTensorInterface* tensor) = 0;
  virtual absl::Status SetAttrStringList(const char* attr_name,
                                         const void* const* values,
                                         const size_t* lengths,

operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer, whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel...

        "//tensorflow/core:reader_base",
        "//tensorflow/core:script_ops_op_lib",
        "//tensorflow/distribute/experimental/rpc/kernels:rpc_ops",
        "//tensorflow/dtensor/cc:dtensor_device_cc",
        "//tensorflow/dtensor/cc:tensor_layout",
        "//tensorflow/lite/c:common",
        "//tensorflow/lite/core/api",
        "//tensorflow/lite/delegates/flex:delegate",

  TFE_TensorHandle* h0_task0 = TestMatrixTensorHandle(ctx);
  TFE_TensorHandle* h1_task0 = TestMatrixTensorHandle(ctx);
  std::vector<TFE_TensorHandle*> handles_task0;
  if (heavy_load_on_streaming_rpc) {
    // Send 50 tensor copy requests to simulate that there have been some RPC
    // requests been enqueued.
    for (int i = 0; i < 50; ++i) {
      handles_task0.push_back(TestMatrixTensorHandle(ctx));
    }
  }

    "@local_config_cuda//cuda:cuda_driver + "\
    "@local_config_cuda//cuda:cudnn + "\
    "@local_config_cuda//cuda:curand + "\
    "@local_config_cuda//cuda:cusolver + "\
    "@local_config_tensorrt//:tensorrt)" --keep_going > $BATS_TEST_TMPDIR/out

  cat <<EOF
There was a path found connecting //tensorflow/tools/pip_package:build_pip_package
to a banned CUDA dependency. Here's the output from bazel query:
EOF

  TFE_DeleteOp(matmul_op);
  // TODO(bgogul): Add some death tests where status is not OK.
}

TEST_F(ShapeInferenceTest, InfersShapesFromInputTensors) {
  // Prepare some tensors for shape.
  TF_Tensor* tensor_1X6 = Int32Tensor({1, 6});
  CHECK_EQ(TF_OK, TF_GetCode(status_)) << TF_Message(status_);
  TF_Tensor* tensor_1X1X6 = Int32Tensor({1, 1, 6});