MXNet: nn.Activation vs nd.relu?

I am new to MXNet (I am using it in Python3)

Their tutorial series encourages you define your own gluon blocks.

So lets say this is your block (a common convolution structure):

class CNN1D(mx.gluon.Block):
    def __init__(self, **kwargs):
        super(CNN1D, self).__init__(**kwargs)
        with self.name_scope():
            self.cnn = mx.gluon.nn.Conv1D(10, 1)
            self.bn = mx.gluon.nn.BatchNorm()
            self.ramp = mx.gluon.nn.Activation(activation='relu')

    def forward(self, x):
        x = mx.nd.relu(self.cnn(x))
        x = mx.nd.relu(self.bn(x))
        x = mx.nd.relu(self.ramp(x))
        return x

This is mirror the structure of their example. What is the difference of mx.nd.relu vs mx.gluon.nn.Activation?

Should it be

x = self.ramp(x)

instead of

x = mx.nd.relu(self.ramp(x))