return op;
}

void ConstHelper(TF_Tensor* t, TF_Graph* graph, TF_Status* s, const char* name,
                 TF_Operation** op) {
  TF_OperationDescription* desc = TF_NewOperation(graph, "Const", name);
  TF_SetAttrTensor(desc, "value", t, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  TF_SetAttrType(desc, "dtype", TF_TensorType(t));
  *op = TF_FinishOperation(desc, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

                              const float* values, const char* name) {
    unique_tensor_ptr tensor(FloatTensor2x2(values), TF_DeleteTensor);
    TF_OperationDescription* desc = TF_NewOperation(graph, "Const", name);
    TF_SetAttrTensor(desc, "value", tensor.get(), s);
    if (TF_GetCode(s) != TF_OK) return nullptr;
    TF_SetAttrType(desc, "dtype", TF_FLOAT);
    TF_Operation* op = TF_FinishOperation(desc, s);

          InvalidArgument("Unparseable TensorShapeProto at index ", i);
      return;
    }
  }
  desc->node_builder.Attr(attr_name, shapes);
  status->status = absl::OkStatus();
}

void TF_SetAttrTensor(TF_OperationDescription* desc, const char* attr_name,
                      TF_Tensor* value, TF_Status* status) {
  Tensor t;
  status->status = TF_TensorToTensor(value, &t);

*   C API: Renamed `TF_Port` to `TF_Output`.
*   C API: The caller retains ownership of `TF_Tensor` objects provided to
    `TF_Run`, `TF_SessionRun`, `TF_SetAttrTensor` etc.
*   Renamed `tf.image.per_image_whitening()` to
    `tf.image.per_image_standardization()`
*   Move Summary protobuf constructors to `tf.summary` submodule.