task: metadata: install: pip install -e .[dev]
patch: diff --git a/pykoop/koopman_pipeline.py b/pykoop/koopman_pipeline.py
index 3dafbb4..821c79d 100644
--- a/pykoop/koopman_pipeline.py
+++ b/pykoop/koopman_pipeline.py
@@ -3425,7 +3425,7 @@ def score_trajectory(
     else:
         score = regression_metric(**regression_metric_args)
     # Return error score if score is not finite
-    if not np.isfinite(score):
+    if not np.all(np.isfinite(score)):
         if isinstance(error_score, str):
             raise ValueError(
                 'Prediction diverged or error occured while scoring.')

problem_statement: `pykoop.score_trajectory()` does not work with `multioutput=raw_values`
```
File "pykoop/koopman_pipeline.py", line 3509, in score_trajectory
    if not np.isfinite(score):
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```
repo: decargroup/pykoop
test_cmd: pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning
test_patch: diff --git a/tests/test_koopman_pipeline.py b/tests/test_koopman_pipeline.py
index bf939a8..cb39b60 100644
--- a/tests/test_koopman_pipeline.py
+++ b/tests/test_koopman_pipeline.py
@@ -323,8 +323,8 @@ class TestKoopmanPipelineScore:
 
     @pytest.mark.parametrize(
         'X_predicted, X_expected, n_steps, discount_factor, '
-        'regression_metric, error_score, min_samples, episode_feature, '
-        'score_exp',
+        'regression_metric, regression_metric_kw, error_score, min_samples, '
+        'episode_feature, score_exp',
         [
             (
                 np.array([
@@ -338,11 +338,32 @@ class TestKoopmanPipelineScore:
                 None,
                 1,
                 'neg_mean_squared_error',
+                None,
                 np.nan,
                 1,
                 False,
                 0,
             ),
+            (
+                np.array([
+                    [1, 2, 3, 4],
+                    [2, 3, 3, 2],
+                ]).T,
+                np.array([
+                    [1, 2, 3, 4],
+                    [2, 3, 3, 2],
+                ]).T,
+                None,
+                1,
+                'neg_mean_squared_error',
+                {
+                    'multioutput': 'raw_values',
+                },
+                np.nan,
+                1,
+                False,
+                np.array([0, 0]),
+            ),
             (
                 np.array([
                     [1, 2],
@@ -355,6 +376,27 @@ class TestKoopmanPipelineScore:
                 None,
                 1,
                 'neg_mean_squared_error',
+                {
+                    'multioutput': 'raw_values',
+                },
+                np.nan,
+                1,
+                False,
+                -np.array([2**2, 1]),
+            ),
+            (
+                np.array([
+                    [1, 2],
+                    [2, 3],
+                ]).T,
+                np.array([
+                    [1, 4],
+                    [2, 2],
+                ]).T,
+                None,
+                1,
+                'neg_mean_squared_error',
+                None,
                 np.nan,
                 1,
                 False,
@@ -370,6 +412,7 @@ class TestKoopmanPipelineScore:
                 None,
                 1,
                 'neg_mean_squared_error',
+                None,
                 np.nan,
                 1,
                 False,
@@ -385,6 +428,7 @@ class TestKoopmanPipelineScore:
                 None,
                 1,
                 'neg_mean_absolute_error',
+                None,
                 np.nan,
                 1,
                 False,
@@ -400,6 +444,7 @@ class TestKoopmanPipelineScore:
                 None,
                 1,
                 'neg_mean_squared_error',
+                None,
                 np.nan,
                 2,
                 False,
@@ -415,6 +460,7 @@ class TestKoopmanPipelineScore:
                 2,
                 1,
                 'neg_mean_squared_error',
+                None,
                 np.nan,
                 1,
                 False,
@@ -430,6 +476,7 @@ class TestKoopmanPipelineScore:
                 None,
                 0.5,
                 'neg_mean_squared_error',
+                None,
                 np.nan,
                 1,
                 False,
@@ -448,6 +495,7 @@ class TestKoopmanPipelineScore:
                 None,
                 1,
                 'neg_mean_squared_error',
+                None,
                 np.nan,
                 1,
                 True,
@@ -465,6 +513,7 @@ class TestKoopmanPipelineScore:
                 1,
                 1,
                 'neg_mean_squared_error',
+                None,
                 np.nan,
                 1,
                 True,
@@ -482,6 +531,7 @@ class TestKoopmanPipelineScore:
                 None,
                 0.5,
                 'neg_mean_squared_error',
+                None,
                 np.nan,
                 1,
                 True,
@@ -499,6 +549,7 @@ class TestKoopmanPipelineScore:
                 1,
                 0.5,
                 'neg_mean_squared_error',
+                None,
                 np.nan,
                 1,
                 True,
@@ -516,6 +567,7 @@ class TestKoopmanPipelineScore:
                 None,
                 1,
                 'neg_mean_squared_error',
+                None,
                 np.nan,
                 1,
                 False,
@@ -533,6 +585,7 @@ class TestKoopmanPipelineScore:
                 None,
                 1,
                 'neg_mean_squared_error',
+                None,
                 -100,
                 1,
                 False,
@@ -550,6 +603,7 @@ class TestKoopmanPipelineScore:
                 None,
                 1,
                 'neg_mean_squared_error',
+                None,
                 'raise',
                 1,
                 False,
@@ -565,6 +619,7 @@ class TestKoopmanPipelineScore:
                 None,
                 1,
                 'neg_mean_squared_error',
+                None,
                 -100,
                 1,
                 False,
@@ -580,6 +635,7 @@ class TestKoopmanPipelineScore:
                 None,
                 1,
                 'neg_mean_squared_error',
+                None,
                 'raise',
                 1,
                 False,
@@ -596,6 +652,7 @@ class TestKoopmanPipelineScore:
                 None,
                 1,
                 'neg_mean_squared_error',
+                None,
                 -100,
                 1,
                 False,
@@ -610,6 +667,7 @@ class TestKoopmanPipelineScore:
         n_steps,
         discount_factor,
         regression_metric,
+        regression_metric_kw,
         error_score,
         min_samples,
         episode_feature,
@@ -623,6 +681,7 @@ class TestKoopmanPipelineScore:
                     n_steps=n_steps,
                     discount_factor=discount_factor,
                     regression_metric=regression_metric,
+                    regression_metric_kw=regression_metric_kw,
                     error_score=error_score,
                     min_samples=min_samples,
                     episode_feature=episode_feature,
@@ -634,6 +693,7 @@ class TestKoopmanPipelineScore:
                 n_steps=n_steps,
                 discount_factor=discount_factor,
                 regression_metric=regression_metric,
+                regression_metric_kw=regression_metric_kw,
                 error_score=error_score,
                 min_samples=min_samples,
                 episode_feature=episode_feature,