From b6e7ee0f97b5b5d15341fac80cccf951b9ba2c01 Mon Sep 17 00:00:00 2001
From: Sait Cakmak <saitcakmak@fb.com>
Date: Thu, 16 Jan 2025 12:16:35 -0500
Subject: [PATCH] remove validation

---
 botorch/optim/optimize_mixed.py   |  2 --
 test/optim/test_optimize_mixed.py | 17 -----------------
 2 files changed, 19 deletions(-)

diff --git a/botorch/optim/optimize_mixed.py b/botorch/optim/optimize_mixed.py
index 105952b3b9..5cbf2227cc 100644
--- a/botorch/optim/optimize_mixed.py
+++ b/botorch/optim/optimize_mixed.py
@@ -526,8 +526,6 @@ def continuous_step(
     """
     bounds = opt_inputs.bounds
     options = opt_inputs.options or {}
-    if (current_x < bounds[0]).any() or (current_x > bounds[1]).any():
-        raise ValueError("continuous_step requires current_x to be within bounds.")
     if len(discrete_dims) == len(current_x):  # nothing continuous to optimize
         with torch.no_grad():
             return current_x, opt_inputs.acq_function(current_x.unsqueeze(0))
diff --git a/test/optim/test_optimize_mixed.py b/test/optim/test_optimize_mixed.py
index f358f0a537..7e2c006a38 100644
--- a/test/optim/test_optimize_mixed.py
+++ b/test/optim/test_optimize_mixed.py
@@ -420,23 +420,6 @@ def test_continuous_step(self):
         self.assertTrue(X is X_out)  # testing pointer equality for due to short cut
         self.assertAllClose(ei_val, ei(X[None]))
 
-        # Input outside of bounds raises error.
-        invalid_X = X.clone()
-        invalid_X[2] = 2
-        with self.assertRaisesRegex(
-            ValueError,
-            "continuous_step requires current_x to be",
-        ):
-            X_new, ei_val = continuous_step(
-                opt_inputs=_make_opt_inputs(
-                    acq_function=ei,
-                    bounds=bounds,
-                    options={"maxiter_continuous": 32},
-                ),
-                discrete_dims=binary_dims,
-                current_x=invalid_X,
-            )
-
     def test_optimize_acqf_mixed_binary_only(self) -> None:
         train_X, train_Y, binary_dims, cont_dims = self._get_data()
         dim = len(binary_dims) + len(cont_dims)