I am extracting the features of an image from an intermediate network using this function
def get_layer_activation(model, name, layer, transform, path):
    activation = {}
    # model = model
    def get_activation(name):
        def hook(model, input, output):
            activation[name] = output.detach()

        return hook

    layer.register_forward_hook(get_activation(name))
    img = tensorify_img(path, transform)
    output = model(img[None, ...].float())  # .to(device)

    return activation[name].reshape(-1)

When I run the function above, it returns different values each time.
_test_transform = transforms.Compose(
        [
            transforms.ToTensor(),
        ]
    )
act = get_layer_activation(
    network_saved, "latent", network_saved.fc1, _test_transform, single_image)

network_saved is the loaded pretrained network from this architecture
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 3)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

Is this an expected behavior? If yes, why?