diff --git a/optax/_src/utils.py b/optax/_src/utils.py
index 75a0ebb79..ba3780cf3 100644
--- a/optax/_src/utils.py
+++ b/optax/_src/utils.py
@@ -21,19 +21,15 @@
 from typing import Any, Optional, Sequence, Union
 
 import chex
-from etils import epy
 import jax
 import jax.numpy as jnp
+import jax.scipy.stats.norm as multivariate_normal
 from optax import tree_utils as otu
 from optax._src import base
 from optax._src import linear_algebra
 from optax._src import numerics
 
 
-with epy.lazy_imports():
-  import jax.scipy.stats.norm as multivariate_normal  # pylint: disable=g-import-not-at-top,ungrouped-imports
-
-
 def tile_second_to_last_dim(a: chex.Array) -> chex.Array:
   ones = jnp.ones_like(a)
   a = jnp.expand_dims(a, axis=-1)
diff --git a/optax/losses/_classification.py b/optax/losses/_classification.py
index 4abbf936f..bf3ad5d3b 100644
--- a/optax/losses/_classification.py
+++ b/optax/losses/_classification.py
@@ -260,11 +260,12 @@ def softmax_cross_entropy(
   Examples:
     >>> import optax
     >>> import jax.numpy as jnp
+    >>> jnp.set_printoptions(precision=4)
     >>> # example: batch_size = 2, num_classes = 3
     >>> logits = jnp.array([[1.2, -0.8, -0.5], [0.9, -1.2, 1.1]])
     >>> labels = jnp.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]])
     >>> print(optax.softmax_cross_entropy(logits, labels))
-    [0.2761297 2.951799 ]
+    [0.2761 2.9518]
 
   References:
     `Cross-entropy Loss <https://en.wikipedia.org/wiki/Cross-entropy>`_,
@@ -329,15 +330,17 @@ def softmax_cross_entropy_with_integer_labels(
   Examples:
     >>> import optax
     >>> import jax.numpy as jnp
+    >>> jnp.set_printoptions(precision=4)
     >>> # example: batch_size = 2, num_classes = 3
     >>> logits = jnp.array([[1.2, -0.8, -0.5], [0.9, -1.2, 1.1]])
     >>> labels = jnp.array([0, 1])
     >>> print(optax.softmax_cross_entropy_with_integer_labels(logits, labels))
-    [0.2761297 2.951799 ]
+    [0.2761 2.9518]
 
     >>> import jax.numpy as jnp
     >>> import numpy as np
     >>> import optax
+    >>> jnp.set_printoptions(precision=4)
     >>> # example: batch_size = (1, 2), num_classes = 12 (i.e. 3 * 4)
     >>> shape = (1, 2, 3, 4)
     >>> logits = jnp.arange(np.prod(shape), dtype=jnp.float32).reshape(shape)
@@ -348,7 +351,7 @@ def softmax_cross_entropy_with_integer_labels(
     >>> cross_entropy = optax.softmax_cross_entropy_with_integer_labels(
     ...     logits, labels, axis=(2, 3))
     >>> print(cross_entropy)
-    [[6.458669   0.45866907]]
+    [[6.4587 0.4587]]
 
   References:
     `Cross-entropy Loss <https://en.wikipedia.org/wiki/Cross-entropy>`_,
diff --git a/optax/losses/_self_supervised.py b/optax/losses/_self_supervised.py
index 5c3a5e22b..cb2d50548 100644
--- a/optax/losses/_self_supervised.py
+++ b/optax/losses/_self_supervised.py
@@ -133,13 +133,14 @@ def triplet_margin_loss(
 
   Examples:
     >>> import jax.numpy as jnp, optax, chex
+    >>> jnp.set_printoptions(precision=4)
     >>> anchors = jnp.array([[0.0, 0.0], [1.0, 1.0]])
     >>> positives = jnp.array([[0.1, 0.1], [1.1, 1.1]])
     >>> negatives = jnp.array([[1.0, 0.0], [0.0, 1.0]])
     >>> output = optax.losses.triplet_margin_loss(anchors, positives, negatives,
     ...                                           margin=1.0)
     >>> print(output)
-    [0.14142442 0.14142442]
+    [0.1414 0.1414]
 
   Args:
     anchors: An array of anchor embeddings, with shape [batch, feature_dim].
diff --git a/optax/projections/_projections.py b/optax/projections/_projections.py
index 91d83ebb3..84e053546 100644
--- a/optax/projections/_projections.py
+++ b/optax/projections/_projections.py
@@ -148,10 +148,10 @@ def projection_simplex(tree: Any, scale: chex.Numeric = 1.0) -> Any:
       >>> import jax.numpy as jnp
       >>> from optax import tree_utils, projections
       >>> tree = {"w": jnp.array([2.5, 3.2]), "b": 0.5}
-      >>> tree_utils.tree_sum(tree)
+      >>> print(tree_utils.tree_sum(tree))
       6.2
       >>> new_tree = projections.projection_simplex(tree)
-      >>> tree_utils.tree_sum(new_tree)
+      >>> print(tree_utils.tree_sum(new_tree))
       1.0000002
 
   .. versionadded:: 0.2.3
@@ -212,11 +212,11 @@ def projection_l1_ball(tree: Any, scale: float = 1.0) -> Any:
       >>> import jax.numpy as jnp
       >>> from optax import tree_utils, projections
       >>> tree = {"w": jnp.array([2.5, 3.2]), "b": 0.5}
-      >>> tree_utils.tree_l1_norm(tree)
-      Array(6.2, dtype=float32)
+      >>> print(tree_utils.tree_l1_norm(tree))
+      6.2
       >>> new_tree = projections.projection_l1_ball(tree)
-      >>> tree_utils.tree_l1_norm(new_tree)
-      Array(1.0000002, dtype=float32)
+      >>> print(tree_utils.tree_l1_norm(new_tree))
+      1.0000002
 
   .. versionadded:: 0.2.4
   """