diff --git a/docs/guides/flax_basics.ipynb b/docs/guides/flax_basics.ipynb
index b76289be82..73a57785a8 100644
--- a/docs/guides/flax_basics.ipynb
+++ b/docs/guides/flax_basics.ipynb
@@ -68,7 +68,7 @@
    "source": [
     "import jax\n",
     "from typing import Any, Callable, Sequence\n",
-    "from jax import lax, random, numpy as jnp\n",
+    "from jax import random, numpy as jnp\n",
     "import flax\n",
     "from flax import linen as nn"
    ]
@@ -775,8 +775,7 @@
     "    kernel = self.param('kernel',\n",
     "                        self.kernel_init, # Initialization function\n",
     "                        (inputs.shape[-1], self.features))  # shape info.\n",
-    "    y = lax.dot_general(inputs, kernel,\n",
-    "                        (((inputs.ndim - 1,), (0,)), ((), ())),) # TODO Why not jnp.dot?\n",
+    "    y = jnp.dot(inputs, kernel)\n",
     "    bias = self.param('bias', self.bias_init, (self.features,))\n",
     "    y = y + bias\n",
     "    return y\n",
@@ -866,7 +865,6 @@
     "    ra_mean = self.variable('batch_stats', 'mean',\n",
     "                            lambda s: jnp.zeros(s),\n",
     "                            x.shape[1:])\n",
-    "    mean = ra_mean.value # This will either get the value or trigger init\n",
     "    bias = self.param('bias', lambda rng, shape: jnp.zeros(shape), x.shape[1:])\n",
     "    if is_initialized:\n",
     "      ra_mean.value = self.decay * ra_mean.value + (1.0 - self.decay) * jnp.mean(x, axis=0, keepdims=True)\n",
diff --git a/docs/guides/flax_basics.md b/docs/guides/flax_basics.md
index 1df7944414..fed965fed4 100644
--- a/docs/guides/flax_basics.md
+++ b/docs/guides/flax_basics.md
@@ -45,7 +45,7 @@ Here we provide the code needed to set up the environment for our notebook.
 
 import jax
 from typing import Any, Callable, Sequence
-from jax import lax, random, numpy as jnp
+from jax import random, numpy as jnp
 import flax
 from flax import linen as nn
 ```
@@ -394,8 +394,7 @@ class SimpleDense(nn.Module):
     kernel = self.param('kernel',
                         self.kernel_init, # Initialization function
                         (inputs.shape[-1], self.features))  # shape info.
-    y = lax.dot_general(inputs, kernel,
-                        (((inputs.ndim - 1,), (0,)), ((), ())),) # TODO Why not jnp.dot?
+    y = jnp.dot(inputs, kernel)
     bias = self.param('bias', self.bias_init, (self.features,))
     y = y + bias
     return y
@@ -448,7 +447,6 @@ class BiasAdderWithRunningMean(nn.Module):
     ra_mean = self.variable('batch_stats', 'mean',
                             lambda s: jnp.zeros(s),
                             x.shape[1:])
-    mean = ra_mean.value # This will either get the value or trigger init
     bias = self.param('bias', lambda rng, shape: jnp.zeros(shape), x.shape[1:])
     if is_initialized:
       ra_mean.value = self.decay * ra_mean.value + (1.0 - self.decay) * jnp.mean(x, axis=0, keepdims=True)