Bring updated approximator_standardization test from upstream

Author: SermetPekin

tests/test_approximators/test_approximator_standardization/test_approximator_standardization.py

@@ -1,5 +1,4 @@
 import keras
-import pytest
 from tests.utils import assert_models_equal
 
 
@@ -17,54 +16,3 @@ def test_save_and_load(tmp_path, approximator, train_dataset, validation_dataset
     loaded = keras.saving.load_model(tmp_path / "model.keras")
 
     assert_models_equal(approximator, loaded)
-
-
-def test_save_and_load_all_variants(
-    tmp_path, adapter, inference_network, summary_network, train_dataset, validation_dataset
-):
-    """Run the same save/load assertions for all `standardize` options in one test node.
-
-    This avoids relying on pytest's bracketed node-id selection; it constructs
-    an approximator for each `standardize` value and runs the same checks.
-    Any failures across variants are aggregated and reported at the end.
-    """
-    from bayesflow import ContinuousApproximator
-
-    standardize_values = [
-        "all",
-        None,
-        "inference_variables",
-        "summary_variables",
-        ("inference_variables", "summary_variables", "inference_conditions"),
-    ]
-
-    failures = []
-
-    for standardize in standardize_values:
-        approximator = ContinuousApproximator(
-            adapter=adapter,
-            inference_network=inference_network,
-            summary_network=summary_network,
-            standardize=standardize,
-        )
-
-        try:
-            data_shapes = keras.tree.map_structure(keras.ops.shape, train_dataset[0])
-            approximator.build(data_shapes)
-            for layer in approximator.standardize_layers.values():
-                assert layer.built
-                for count in layer.count:
-                    assert count == 0.0
-            approximator.compute_metrics(**train_dataset[0])
-
-            model_path = tmp_path / f"model_{str(standardize)}.keras"
-            keras.saving.save_model(approximator, model_path)
-            loaded = keras.saving.load_model(model_path)
-
-            assert_models_equal(approximator, loaded)
-        except Exception as exc:  # collect failures and continue
-            failures.append((standardize, repr(exc)))
-
-    if failures:
-        msgs = ", ".join([f"{s!r}: {m}" for s, m in failures])
-        pytest.fail(f"One or more standardize variants failed: {msgs}")