diff --git a/resnet_cifar10.py b/resnet_cifar10.py
index 3d2a556..26e7ff2 100755
--- a/resnet_cifar10.py
+++ b/resnet_cifar10.py
@@ -146,6 +146,7 @@ class ResidualBlock(eqx.Module):
                                     eps=0.001)
         self.bn2 = eqx.nn.BatchNorm(in_channels, "batch", momentum=0.9,
                                     eps=0.001)
+        # TODO: is bn2 in_channels correct?
         self.conv1 = eqx.nn.Conv2d(in_channels, out_channels, kernel_size=3,
                                    stride=stride, key=keys[0])
         self.conv2 = eqx.nn.Conv2d(out_channels, out_channels, kernel_size=3,
@@ -182,6 +183,76 @@ class ResidualBlock(eqx.Module):
         return x
 
 
+class BottleneckBlock(eqx.Module):
+    bn1: eqx.nn.BatchNorm
+    bn2: eqx.nn.BatchNorm
+    bn3: eqx.nn.BatchNorm
+    conv1: eqx.nn.Conv2d
+    conv2: eqx.nn.Conv2d
+    conv3: eqx.nn.Conv2d
+    project_conv: eqx.nn.Conv2d
+    relu_leak: float
+    is_b4_res: bool
+
+    def __init__(self, in_channels: int, out_channels: int, stride: int,
+                 relu_leak: float, is_b4_res: bool, key):
+        self.stride = stride
+        self.relu_leak = relu_leak
+        self.is_b4_res = is_b4_res
+
+        keys = jax.random.split(key, 4)
+
+        # TODO: channels might be wrong for 2 and 3
+        self.bn1 = eqx.nn.BatchNorm(in_channels, "batch", momentum=0.9,
+                                    eps=0.001)
+        self.bn2 = eqx.nn.BatchNorm(in_channels, "batch", momentum=0.9,
+                                    eps=0.001)
+        self.bn3 = eqx.nn.BatchNorm(in_channels, "batch", momentum=0.9,
+                                    eps=0.001)
+
+        # TODO: /4 seems like it wouldn't work, maybe // or ceil?
+        # TODO: what's up with the lack of padding?
+        # TODO: does sride make sense for conv 2&3?
+        mid_channels = out_channels / 4
+        self.conv1 = eqx.nn.Conv2d(in_channels, mid_channels, kernel_size=1,
+                                   stride=stride, key=keys[0])
+        self.conv2 = eqx.nn.Conv2d(mid_channels, mid_channels, kernel_size=3,
+                                   stride=[1,1,1,1], key=keys[1])
+        self.conv3 = eqx.nn.Conv2d(mid_channels, out_channels, kernel_size=1,
+                                   stride=[1,1,1,1], key=keys[2])
+
+        if in_channels != out_channels:
+            self.project = eqx.nn.Conv2d(in_channels, out_channels,
+                           kernel_size=1, stride=stride, key=keys[3])
+        else:
+            self.project = None
+
+    def __call__(
+        self,
+        x: Float[Array, "batch channels w h"]
+    ) -> Float[Array, "batch channels w h"]:
+        if self.is_b4_res:
+            x = self.bn1(x)
+            x = jax.nn.leaky_relu(x, negative_slope=self.relu_leak)
+            orig_x = x
+        else:
+            orig_x = x
+            x = self.bn1(x)
+            x = jax.nn.leaky_relu(x, negative_slope=self.relu_leak)
+
+        x = self.conv1(x)
+        x = self.bn2(x)
+        x = jax.nn.leaky_relu(x, negative_slope=self.relu_leak)
+        x = self.conv2(x)
+        x = self.bn3(x)
+        x = jax.nn.leaky_relu(x, negative_slope=self.relu_leak)
+        x = self.conv3(x)
+
+        if self.project:
+            orig_x = self.project(orig_x)
+
+        x += orig_x
+        return x
 
 
 class ResNet(eqx.Module):