在MXNet中使用Capsule Networks，可以通過CapsuleLayer和CapsuleLoss這兩個API來實現。首先需要定義CapsuleLayer，然后使用CapsuleLoss來定義損失函數。

以下是一個簡單的示例代碼：

import mxnet as mx
from mxnet.gluon import nn
from mxnet import nd

class CapsuleLayer(nn.HybridBlock):
    def __init__(self, num_capsules, num_route_nodes, in_channels, out_channels, num_iterations=3, **kwargs):
        super(CapsuleLayer, self).__init__(**kwargs)
        self.num_route_nodes = num_route_nodes
        self.num_iterations = num_iterations
        with self.name_scope():
            self.W = self.params.get('weight', shape=(1, num_route_nodes, num_capsules, in_channels, out_channels))

    def hybrid_forward(self, F, x):
        batch_size = x.shape[0]
        x = x.expand_dims(axis=2).broadcast_to((batch_size, self.num_route_nodes, x.shape[1], x.shape[2]))
        W = self.W.data().expand_dims(axis=0)
        u_hat = F.linalg.gemm2(x, W, transpose_b=True)
        u_hat_stopped = F.stop_gradient(u_hat)
        b = nd.zeros((batch_size, self.num_route_nodes, self.num_capsules, 1))
        for i in range(self.num_iterations):
            c = F.softmax(b, axis=2)
            s = F.broadcast_mul(c, u_hat)
            s = F.sum(s, axis=1, keepdims=True)
            v = self.squash(s)
            if i < self.num_iterations - 1:
                b = b + nd.sum(u_hat_stopped * v, axis=-1, keepdims=True)
        return v

    def squash(self, x):
        norm = nd.sum(x ** 2, axis=-1, keepdims=True)
        return (norm / (1 + norm)) * (x / nd.sqrt(norm + 1e-8))

class CapsuleLoss(nn.HybridBlock):
    def __init__(self, lambda_val=0.5, **kwargs):
        super(CapsuleLoss, self).__init__(**kwargs)
        self.lambda_val = lambda_val

    def hybrid_forward(self, F, v, labels):
        v_norm = nd.sqrt(nd.sum(v ** 2, axis=-1, keepdims=True))
        left = labels * F.relu(0.9 - v_norm) ** 2
        right = self.lambda_val * (1 - labels) * F.relu(v_norm - 0.1) ** 2
        loss = F.sum(left + right, axis=-1)
        return loss

然后可以通過定義一個包含CapsuleLayer和CapsuleLoss的網絡來使用Capsule Networks。需要注意的是，Capsule Networks通常用于處理視覺任務，比如圖像分類或目標檢測。

net = nn.Sequential()
net.add(CapsuleLayer(num_capsules=10, num_route_nodes=32, in_channels=8, out_channels=16))
net.add(CapsuleLayer(num_capsules=10, num_route_nodes=32, in_channels=16, out_channels=16))
net.add(CapsuleLoss())

# 訓練模型
# ...

這樣就可以在MXNet中使用Capsule Networks進行訓練和預測。需要根據具體的任務和數據來調整網絡結構和參數。