diff --git a/example/glow2.py b/example/glow2.py
new file mode 100644
index 0000000..0eadcee
--- /dev/null
+++ b/example/glow2.py
@@ -0,0 +1,84 @@
+import numpy as np
+import torch
+import torchvision
+from tqdm import tqdm
+
+import normflow as nf
+
+# Construct Model
+# Define flows
+L = 3
+K = 16
+torch.manual_seed(0)
+
+input_shape = (3, 32, 32)
+channels = 3
+hidden_channels = 256
+split_mode = 'channel'
+scale = True
+num_classes = 10
+# Set up flows, distributions and merge operations
+q0 = []
+merges = []
+flows = []
+for i in range(L):
+    flows_ = []
+    for j in range(K):
+        flows_ += [nf.flows.GlowBlock(channels * 2 ** (L + 1 - i), hidden_channels,
+                                      split_mode=split_mode, scale=scale)]
+    flows_ += [nf.flows.Squeeze()]
+    flows += [flows_]
+    latent_shape = (input_shape[0] * 2 ** (L - i), input_shape[1] // 2 ** (L - i),
+                    input_shape[2] // 2 ** (L - i))
+    if i > 0:
+        merges += [nf.flows.Merge()]
+        latent_shape = (input_shape[0] * 2 ** (L - i), input_shape[1] // 2 ** (L - i),
+                        input_shape[2] // 2 ** (L - i))
+    else:
+        latent_shape = (input_shape[0] * 2 ** (L + 1), input_shape[1] // 2 ** L,
+                        input_shape[2] // 2 ** L)
+    q0 += [nf.distributions.ClassCondDiagGaussian(latent_shape, num_classes)]
+
+# Construct flow model
+model = nf.MultiscaleFlow(q0, flows, merges)
+# End Construct Model
+logit = nf.utils.Logit(alpha=0.05)
+# transform = transforms.Compose(
+#     [transforms.ToTensor(),
+#      transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+device = torch.device('cpu')
+transform = torchvision.transforms.Compose(
+    [torchvision.transforms.ToTensor(), nf.utils.Jitter(), logit, nf.utils.ToDevice(device)])
+batch_size = 4
+
+trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
+                                        download=True, transform=transform)
+trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
+                                          shuffle=True, num_workers=2)
+
+testset = torchvision.datasets.CIFAR10(root='./data', train=False,
+                                       download=True, transform=transform)
+testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
+                                         shuffle=False, num_workers=2)
+
+classes = ('plane', 'car', 'bird', 'cat',
+           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
+train_iter = iter(trainloader)
+
+# Train model
+max_iter = 2000
+
+loss_hist = np.array([])
+
+optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-5)
+
+for i in tqdm(range(max_iter)):
+    try:
+        x, y = next(train_iter)
+    except StopIteration:
+        train_iter = iter(trainloader)
+        x, y = next(train_iter)
+    print(x.shape)
+    print(type(x))
+    print(y.shape)
+    print(type(y))