Fixes RST link formatting.

docs/notebooks/annotated_mnist.ipynb

@@ -71,11 +71,11 @@
  "## 2. Define network\n",
  "\n",
  "Create a convolutional neural network with the Linen API by subclassing\n",
- "[`Module`](https://flax.readthedocs.io/en/latest/flax.linen.html#core-module-abstraction).\n",
+ "[Module](https://flax.readthedocs.io/en/latest/flax.linen.html#core-module-abstraction).\n",
  "Because the architecture in this example is relatively simple—you're just\n",
  "stacking layers—you can define the inlined submodules directly within the\n",
  "`__call__` method and wrap it with the\n",
- "[`@compact`](https://flax.readthedocs.io/en/latest/flax.linen.html#compact-methods)\n",
+ "[@compact](https://flax.readthedocs.io/en/latest/flax.linen.html#compact-methods)\n",
  "decorator."
  ]
  },
@@ -115,10 +115,10 @@
  "## 3. Define loss\n",
  "\n",
  "Define a cross-entropy loss function using just\n",
- "[`jax.numpy`](https://jax.readthedocs.io/en/latest/jax.numpy.html)\n",
+ "[jax.numpy](https://jax.readthedocs.io/en/latest/jax.numpy.html)\n",
  "that takes the model's logits and label vectors and returns a scalar loss. The\n",
  "labels can be one-hot encoded with\n",
- "[`jax.nn.one_hot`](https://jax.readthedocs.io/en/latest/_autosummary/jax.nn.one_hot.html),\n",
+ "[jax.nn.one_hot](https://jax.readthedocs.io/en/latest/_autosummary/jax.nn.one_hot.html),\n",
  "as demonstrated below.\n",
  "\n",
  "Note that for demonstration purposes, we return `nn.log_softmax()` from\n",
@@ -249,17 +249,17 @@
  "\n",
  "- Evaluates the neural network given the parameters and a batch of input images\n",
  " with the\n",
- " [`Module.apply`](https://flax.readthedocs.io/en/latest/flax.linen.html#flax.linen.Module.apply)\n",
+ " [Module.apply](https://flax.readthedocs.io/en/latest/flax.linen.html#flax.linen.Module.apply)\n",
  " method.\n",
  "- Computes the `cross_entropy_loss` loss function.\n",
  "- Evaluates the loss function and its gradient using\n",
- " [`jax.value_and_grad`](https://jax.readthedocs.io/en/latest/jax.html#jax.value_and_grad).\n",
+ " [jax.value_and_grad](https://jax.readthedocs.io/en/latest/jax.html#jax.value_and_grad).\n",
  "- Applies a\n",
  " [pytree](https://jax.readthedocs.io/en/latest/pytrees.html#pytrees-and-jax-functions)\n",
  " of gradients to the optimizer to update the model's parameters.\n",
  "- Computes the metrics using `compute_metrics` (defined earlier).\n",
  "\n",
- "Use JAX's [`@jit`](https://jax.readthedocs.io/en/latest/jax.html#jax.jit)\n",
+ "Use JAX's [@jit](https://jax.readthedocs.io/en/latest/jax.html#jax.jit)\n",
  "decorator to trace the entire `train_step` function and just-in-time compile\n",
  "it with [XLA](https://www.tensorflow.org/xla) into fused device operations\n",
  "that run faster and more efficiently on hardware accelerators."
@@ -296,7 +296,7 @@
  "## 8. Evaluation step\n",
  "\n",
  "Create a function that evaluates your model on the test set with\n",
- "[`Module.apply`](https://flax.readthedocs.io/en/latest/flax.linen.html#flax.linen.Module.apply)"
+ "[Module.apply](https://flax.readthedocs.io/en/latest/flax.linen.html#flax.linen.Module.apply)"
  ]
  },
  {
@@ -324,7 +324,7 @@
  "Define a training function that:\n",
  "\n",
  "- Shuffles the training data before each epoch using\n",
- " [`jax.random.permutation`](https://jax.readthedocs.io/en/latest/_autosummary/jax.random.permutation.html)\n",
+ " [jax.random.permutation](https://jax.readthedocs.io/en/latest/_autosummary/jax.random.permutation.html)\n",
  " that takes a PRNGKey as a parameter (check the\n",
  " [JAX - the sharp bits](https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#JAX-PRNG)).\n",
  "- Runs an optimization step for each batch.\n",