Remove usage of kwargs in jax.numpy.clip

src/jaxsim/integrators/variable_step.py

@@ -312,9 +312,9 @@ def __call__(self, x0: State, t0: Time, dt: TimeStep, **kwargs) -> NextState:
 
         # Clip the estimated initial step size to the given bounds, if necessary.
         self.params["dt0"] = jnp.clip(
-            a=self.params["dt0"],
-            a_min=jnp.minimum(self.dt_min, self.params["dt0"]),
-            a_max=jnp.minimum(self.dt_max, self.params["dt0"]),
+            self.params["dt0"],
+            jnp.minimum(self.dt_min, self.params["dt0"]),
+            jnp.minimum(self.dt_max, self.params["dt0"]),
         )
 
         # =========================================================
@@ -371,7 +371,7 @@ def while_loop_body(carry: Carry) -> Carry:
 
             # Shrink the Δt every time by the safety factor (even when accepted).
             # The β parameters define the bounds of the timestep update factor.
-            safety = jnp.clip(self.safety, a_min=0.0, a_max=1.0)
+            safety = jnp.clip(self.safety, 0.0, 1.0)
             β_min = jnp.maximum(0.0, self.beta_min)
             β_max = jnp.maximum(β_min, self.beta_max)
 
@@ -383,9 +383,9 @@ def while_loop_body(carry: Carry) -> Carry:
             # In case of acceptance, Δt_next could either be larger than Δt0,
             # or slightly smaller than Δt0 depending on the safety factor.
             Δt_next = Δt0 * jnp.clip(
-                a=safety * jnp.power(1 / local_error, 1 / (q + 1)),
-                a_min=β_min,
-                a_max=β_max,
+                safety * jnp.power(1 / local_error, 1 / (q + 1)),
+                β_min,
+                β_max,
             )
 
             def accept_step():