TF_TensorDefaultAlignment(), num_bytes));
  int64_t dims[] = {2, 3};
  bool deallocator_called = false;
  TF_Tensor* t = TF_NewTensor(TF_FLOAT, dims, 2, values, num_bytes,
                              &Deallocator, &deallocator_called);
  EXPECT_FALSE(deallocator_called);
  EXPECT_EQ(TF_FLOAT, TF_TensorType(t));
  EXPECT_EQ(2, TF_NumDims(t));
  EXPECT_EQ(dims[0], TF_Dim(t, 0));

  // Method to copy a tensor from the custom device to a target device.
  TFE_TensorHandle* (*copy_tensor_from_device)(TFE_Context* context,
                                               TFE_TensorHandle* tensor,
                                               const char* target_device_name,
                                               TF_Status* status,

// A create function. This will never actually get called in this test, it's
// just nice to know that it compiles.
void* create(TF_OpKernelConstruction* ctx) {
  TF_DataType type;
  TF_Status* s = TF_NewStatus();
  TF_OpKernelConstruction_GetAttrType(ctx, "foobar", &type, s);
  TF_DeleteStatus(s);
  return NULL;
}

// A compute function. This will never actually get called in this test, it's
// just nice to know that it compiles.

  TracingContext* tracing_ctx = dyn_cast<TracingContext>(unwrap(func));
  if (!tracing_ctx) {
    tsl::Set_TF_Status_from_Status(
        s, tensorflow::errors::InvalidArgument(
               "TF_AddFunctionParameter must be called on a TracingContext."));
    return nullptr;
  }
  tensorflow::PartialTensorShape partial_shape;
  if (shape.num_dims != -1) {
    DCHECK(shape.dim_sizes != nullptr);

  virtual Status InitializeLocalOnlyContext(const ServerDef& server_def,
                                            int keep_alive_secs) = 0;

  // Set up a multi-client distributed execution environment. Must be called
  // on all tasks in the cluster. This call internally coordinates with other
  // tasks to initialize the eager context and TF server for multi-client
  // execution.

-   In such situations, we may request you to make further changes to your PR
    for the tests to pass.
-   Once the tests pass, we now bring all the code into the internal code base,
    using a job called "copybara".

**5. Copy to Google Internal codebase and run internal CI**

-   Once the PR is in the Google codebase, we make sure it integrates well with
    its dependencies and the rest of the system.

  for (int i = 0; i < noutputs; ++i) {
    output_names[i] = c_output_names[i];
  }
  std::vector<string> target_oper_names(ntargets);
  for (int i = 0; i < ntargets; ++i) {
    target_oper_names[i] = c_target_oper_names[i];
  }
  TF_Run_Helper(s->session, nullptr, run_options, input_pairs, output_names,
                c_outputs, target_oper_names, run_metadata, status);
}

void TF_PRunSetup(TF_DeprecatedSession* s,

    if (op_) {
      return errors::FailedPrecondition("Reset called on already built op.");
    }
    if (raw_device_name) {
      device_name_ = raw_device_name;
    }
    op_type_ = op;
    return absl::OkStatus();
  }
  Status SetOpName(const char* const op_name) override {
    if (op_) {
      return errors::FailedPrecondition(
          "SetOpName called on already built op.");
    }
    if (op_type_.empty()) {

      ImmediateExecutionTensorHandle* handle,
      const tensorflow::string& target_device_name,
      ImmediateExecutionTensorHandle** result) override {
    TF_Status status;
    handle->Ref();
    TFE_TensorHandle* result_handle = device_.copy_tensor_from_device(
        context_, tensorflow::wrap(handle), target_device_name.c_str(), &status,
        info_);
    handle->Unref();

*   <IF RELEASE CONTAINS MULTIPLE FEATURES FROM SAME AREA, GROUP THEM TOGETHER>

*   Add `is_cpu_target_available`, which indicates whether or not TensorFlow was
    built with support for a given CPU target. This can be useful for skipping
    target-specific tests if a target is not supported.

*   `tf.data`
    * Support `data.experimental.distribued_save`. `distribued_save` uses
      tf.data service