You can use @Supreet’s code to return the outputs (just add commas between the returned tensors).
Once you grab the outputs, you could save them to a .csv.
Here is a small example:
class Autoencoder(nn.Module):
    def __init__(self, ):
        super(Autoencoder, self).__init__()
        self.fc1 = nn.Linear(100, 50)
        self.fc2 = nn.Linear(50, 20)
        self.fc3 = nn.Linear(20, 5)
        self.fc4 = nn.Linear(5, 1)
        self.fc5 = nn.Linear(1, 5)
        self.fc6 = nn.Linear(5, 20)
        self.fc7 = nn.Linear(20, 50)
        self.fc8 = nn.Linear(50, 100)
        self.relu = nn.ReLU()

    def forward(self, x):
        x1 = self.relu(self.fc1(x))
        x2 = self.relu(self.fc2(x1))
        x3 = self.relu(self.fc3(x2))
        x4 = self.relu(self.fc4(x3))
        x5 = self.relu(self.fc5(x4))
        x6 = self.relu(self.fc6(x5))
        x7 = self.relu(self.fc7(x6))
        x8 = self.relu(self.fc8(x7))
        return x1, x2, x3, x4, x5, x6, x7, x8


model = Autoencoder()
x = torch.randn(1, 100)
outputs = model(x)
for i, output in enumerate(outputs):
    np.savetxt(
        'output{}.csv'.format(i),
        output.detach().numpy(),
        delimiter=',')