Function MakeArgTuple

tensorflow/python/lib/core/py_func.cc:78–111

Givens the 'call', prepares the token and inputs as a python tuple that is appropriate for calling the trampoline.

// Givens the 'call', prepares the token and inputs as a python tuple
// that is appropriate for calling the trampoline.
Status MakeArgTuple(const PyCall* call, EagerContext* ctx, PyObject** tuple) {
  int64 n = call->ins.size();
  PyObject* lst = PyList_New(n);
  CHECK(lst);
  // TFE_TensorHandle assumes that CPU is identified by nullptr.
  Device* device = IsCPUDevice(call->device) ? nullptr : call->device;
  for (int64 i = 0; i < n; ++i) {
    PyObject* arg = nullptr;
    const Tensor& t = call->ins[i];
    if (call->eager) {
      TensorHandle* handle;
      TF_RETURN_IF_ERROR(TensorHandle::CreateLocalHandle(
          t, ctx->CanonicalDevice(device), ctx, &handle));
      arg = EagerTensorFromHandle(new TFE_TensorHandle(handle));
      if (arg == nullptr) {
        Py_DECREF(lst);
        return errors::Internal("Unable to procure EagerTensor from Tensor.");
      }
    } else {
      Status s = TensorToNdarray(t, &arg);
      if (!s.ok()) {
        Py_DECREF(lst);
        return s;
      }
      arg = PyArray_Return(reinterpret_cast<PyArrayObject*>(arg));
    }
    PyList_SetItem(lst, i, arg);
  }
  const char* device_name =
      device == nullptr ? nullptr : device->attributes().name().c_str();
  *tuple = Py_BuildValue("(ssN)", call->token.c_str(), device_name, lst);
  CHECK(*tuple);
  return Status::OK();
}

bool IsSingleNone(PyObject* obj) {
  if (!PyArray_Check(obj)) {