Merge branch 'main' into fix-index-tensor-on-cpu

.github/CODEOWNERS

@@ -3,7 +3,3 @@
 # Documentation
 /docs/                  @Safe-DS/Library
 /mkdocs.yml             @Safe-DS/Library
-
-# Code
-/src/                   @lars-reimann @Gerhardsa0 @Marsmaennchen221 @sibre28
-/tests/                 @lars-reimann @Gerhardsa0 @Marsmaennchen221 @sibre28

src/safeds/data/tabular/containers/_table.py

@@ -1007,22 +1007,22 @@ def transform_column(
     # ------------------------------------------------------------------------------------------------------------------
 
     @overload
-    def count_row_if(
+    def count_rows_if(
         self,
         predicate: Callable[[Row], Cell[bool | None]],
         *,
         ignore_unknown: Literal[True] = ...,
     ) -> int: ...
 
     @overload
-    def count_row_if(
+    def count_rows_if(
         self,
         predicate: Callable[[Row], Cell[bool | None]],
         *,
         ignore_unknown: bool,
     ) -> int | None: ...
 
-    def count_row_if(
+    def count_rows_if(
         self,
         predicate: Callable[[Row], Cell[bool | None]],
         *,
@@ -1059,10 +1059,10 @@ def count_row_if(
         --------
         >>> from safeds.data.tabular.containers import Table
         >>> table = Table({"col1": [1, 2, 3], "col2": [1, 3, 3]})
-        >>> table.count_row_if(lambda row: row["col1"] == row["col2"])
+        >>> table.count_rows_if(lambda row: row["col1"] == row["col2"])
         2
 
-        >>> table.count_row_if(lambda row: row["col1"] > row["col2"])
+        >>> table.count_rows_if(lambda row: row["col1"] > row["col2"])
         0
         """
         expression = predicate(_LazyVectorizedRow(self))._polars_expression

src/safeds/ml/nn/_model.py

@@ -111,7 +111,7 @@ def __init__(
         self._total_number_of_epochs_done = 0
 
     @staticmethod
-    def load_pretrained_model(huggingface_repo: str) -> NeuralNetworkRegressor:  # pragma: no cover
+    def from_pretrained_model(huggingface_repo: str) -> NeuralNetworkRegressor:  # pragma: no cover
         """
         Load a pretrained model from a [Huggingface repository](https://huggingface.co/models/).
 
@@ -703,7 +703,7 @@ def __init__(
         self._total_number_of_epochs_done = 0
 
     @staticmethod
-    def load_pretrained_model(huggingface_repo: str) -> NeuralNetworkClassifier:  # pragma: no cover
+    def from_pretrained_model(huggingface_repo: str) -> NeuralNetworkClassifier:  # pragma: no cover
         """
         Load a pretrained model from a [Huggingface repository](https://huggingface.co/models/).
 

tests/safeds/data/tabular/containers/_table/test_count_row_if.py → tests/safeds/data/tabular/containers/_table/test_count_rows_if.py

@@ -1,4 +1,5 @@
 import pytest
+
 from safeds.data.tabular.containers import Table
 
 
@@ -30,7 +31,7 @@ def test_should_handle_boolean_logic(
     expected: int,
 ) -> None:
     table = Table({"a": values})
-    assert table.count_row_if(lambda row: row["a"] < 2) == expected
+    assert table.count_rows_if(lambda row: row["a"] < 2) == expected
 
 
 @pytest.mark.parametrize(
@@ -61,4 +62,4 @@ def test_should_handle_kleene_logic(
     expected: int | None,
 ) -> None:
     table = Table({"a": values})
-    assert table.count_row_if(lambda row: row["a"] < 2, ignore_unknown=False) == expected
+    assert table.count_rows_if(lambda row: row["a"] < 2, ignore_unknown=False) == expected