Import a simple Tensorflow frozen_model.pb file and make prediction in C++

I am trying to import a graph I exported from Tensorflow Python into Tensorflow C++. I've already successfully re-imported the graph into Python. The only thing I want now is to write the same code in C++ but I am not sure about the C++ api functions and there usage as the documentation on the Tensorflow website is not good enough.

Here's the C++ code I found so far.

C++:

namespace tf = tensorflow;

tf::Session* session;

tf::Status status = tf::NewSession(tf::SessionOptions(), &session);
checkStatus(status);

tf::GraphDef graph_def;
status = ReadBinaryProto(tf::Env::Default(), "./models/frozen_model.pb", &graph_def);
checkStatus(status);

status = session->Create(graph_def);
checkStatus(status);

tf::Tensor x(tf::DT_FLOAT, tf::TensorShape());
tf::Tensor y(tf::DT_FLOAT, tf::TensorShape());

x.scalar<float>()() = 23.0;
y.scalar<float>()() = 19.0;

std::vector<std::pair<tf::string, tf::Tensor>> input_tensors = {{"x", x}, {"y", y}};
std::vector<string> vNames; // vector of names for required graph nodes
vNames.push_back("prefix/input_neurons:0");
vNames.push_back("prefix/prediction_restore:0");
std::vector<tf::Tensor> output_tensors;

status = session->Run({}, vNames,  {}, &output_tensors);
checkStatus(status);

tf::Tensor output = output_tensors[0];
std::cout << "Success: " << output.scalar<float>() << "!" << std::endl;
session->Close();
return 0;

The problem I am having with the current c++ code above is that it says it cannot find any operation by the name of prefix/input_neurons:0. Although there is an operation in the graph because when i import this graph in the Python code (shown below), it works perfectly fine.

Here's the Python code to import the graph successfully.

Python: ( Works perfectly fine )

def load_graph(frozen_graph_filename):
    # We load the protobuf file from the disk and parse it to retrieve the 
    # unserialized graph_def
    with tf.gfile.GFile(frozen_graph_filename, "rb") as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())

    # Then, we can use again a convenient built-in function to import a graph_def into the 
    # current default Graph
    with tf.Graph().as_default() as graph:
        tf.import_graph_def(
            graph_def, 
            input_map=None, 
            return_elements=None, 
            name="prefix", 
            op_dict=None, 
            producer_op_list=None
        )
    return graph

# We use our "load_graph" function
graph = load_graph("./models/frozen_model.pb")

# We can verify that we can access the list of operations in the graph
for op in graph.get_operations():
    print(op.name)     # <--- printing the operations snapshot below
    # prefix/Placeholder/inputs_placeholder
    # ...
    # prefix/Accuracy/predictions

# We access the input and output nodes
x = graph.get_tensor_by_name('prefix/input_neurons:0')
y = graph.get_tensor_by_name('prefix/prediction_restore:0')

# We launch a Session
with tf.Session(graph=graph) as sess:

    test_features = [[0.377745556,0.009904444,0.063231111,0.009904444,0.003734444,0.002914444,0.008633333,0.000471111,0.009642222,0.05406,0.050163333,7e-05,0.006528889,0.000314444,0.00649,0.043956667,0.016816667,0.001644444,0.016906667,0.00204,0.027342222,0.13864]]
        # compute the predicted output for test_x
    pred_y = sess.run( y, feed_dict={x: test_features} )
    print(pred_y)

Update

I can print the operations from the python script. Here's the screenshot.

Here's the error I get.