feat(patch extractor factory): remove unecessary kwarg builder func

src/careamics/dataset_ng/patch_extractor/patch_extractor_factory.py

@@ -1,6 +1,6 @@
 from collections.abc import Sequence
 from pathlib import Path
-from typing import Any, Optional, Union
+from typing import Optional, Union
 
 from numpy.typing import NDArray
 
@@ -12,19 +12,6 @@
 )
 
 
-def build_patch_extractor_constructor_kwargs(
-    data_config: GeneralDataConfig, **custom_kwargs: Any
-) -> dict:  # TODO: return union of TypedDicts?
-    if data_config.data_type == SupportedData.ARRAY:
-        return {"axes": data_config.axes}
-    elif data_config.data_type == SupportedData.TIFF:
-        return {"axes": data_config.axes}
-    elif data_config.data_type == SupportedData.CUSTOM:
-        return {"axes": data_config.axes, **custom_kwargs}
-    else:
-        raise ValueError(f"Data type {data_config.data_type} is not supported.")
-
-
 def get_patch_extractor_constructor(
     data_config: GeneralDataConfig,
 ) -> PatchExtractorConstructor:
@@ -44,7 +31,7 @@ def create_patch_extractors(
     val_data: Optional[Union[Sequence[NDArray], Sequence[Path]]] = None,
     train_data_target: Optional[Union[Sequence[NDArray], Sequence[Path]]] = None,
     val_data_target: Optional[Union[Sequence[NDArray], Sequence[Path]]] = None,
-    **custom_kwargs,
+    **kwargs,
 ) -> tuple[
     PatchExtractor,
     Optional[PatchExtractor],
@@ -56,14 +43,13 @@ def create_patch_extractors(
     constructor = get_patch_extractor_constructor(data_config)
 
     # build key word args
-    constructor_kwargs = build_patch_extractor_constructor_kwargs(
-        data_config, **custom_kwargs
-    )
-    # --- train images
+    constructor_kwargs = {"axes": data_config.axes, **kwargs}
+
+    # --- train data extractor
     train_patch_extractor: PatchExtractor = constructor(
         source=train_data, **constructor_kwargs
     )
-
+    # --- additional data extractors
     additional_patch_extractors: list[Union[PatchExtractor, None]] = []
     additional_data_sources = [val_data, train_data_target, val_data_target]
     for data_source in additional_data_sources: