diff --git a/flowjax/bijections/utils.py b/flowjax/bijections/utils.py
index 6721ced..2554686 100644
--- a/flowjax/bijections/utils.py
+++ b/flowjax/bijections/utils.py
@@ -6,6 +6,7 @@
 
 import equinox as eqx
 import jax.numpy as jnp
+import lineax as lx
 import numpy as np
 from jaxtyping import Array, Int
 
@@ -13,7 +14,6 @@
 from flowjax.bijections.chain import Chain
 from flowjax.utils import arraylike_to_array, check_shapes_match, merge_cond_shapes
 
-import lineax as lx
 
 class Invert(AbstractBijection):
     """Invert a bijection.
@@ -301,6 +301,7 @@ def __init__(
         self,
         bijection: AbstractBijection,
         inverter: Callable[[AbstractBijection, Array, Array | None], Array],
+        *,
         use_implicit_differentation: bool = True,
     ):
         self.bijection = bijection
@@ -312,25 +313,6 @@ def __init__(
         else:
             self.inverter = inverter
 
-    @staticmethod
-    def _wrap_inverter_with_error_on_grad(inverter):
-        @eqx.filter_custom_jvp
-        def wrapped_inverter(bijection, y, condition=None):
-            return inverter(bijection, y, condition)
-
-        @wrapped_inverter.def_jvp
-        def wrapped_inverter_jvp(*_args, **_kwargs):
-            raise RuntimeError(
-                "Computing gradients through the numerical inverse would lead to "
-                "misleading results. If you are using a flow with the analytical "
-                "transform only defined in one direction, consider inverting the "
-                "bijection by flipping the ``invert`` argument to the flow. If this is "
-                "not possible, consider using implicit differentation (not yet "
-                "supported)."
-            )
-
-        return wrapped_inverter
-
     @staticmethod
     def _wrap_inverter_with_implicit_jvp(inverter):
         @eqx.filter_custom_jvp