feat: move NN converters and layers to separate packages (#759)

### Summary of Changes

NN converters and layers are now in separate packages. We can later
re-export them from the `safeds.ml.nn` package based on user feedback.

src/safeds/ml/nn/__init__.py

@@ -1,75 +1,21 @@
-"""Classes for classification tasks."""
+"""Neural networks for various tasks."""
 
 from typing import TYPE_CHECKING
 
 import apipkg
 
 if TYPE_CHECKING:
-    from ._convolutional2d_layer import Convolutional2DLayer, ConvolutionalTranspose2DLayer
-    from ._flatten_layer import FlattenLayer
-    from ._forward_layer import ForwardLayer
-    from ._input_conversion import InputConversion
-    from ._input_conversion_image import InputConversionImage
-    from ._input_conversion_table import InputConversionTable
-    from ._input_conversion_time_series import InputConversionTimeSeries
-    from ._layer import Layer
-    from ._lstm_layer import LSTMLayer
     from ._model import NeuralNetworkClassifier, NeuralNetworkRegressor
-    from ._output_conversion import OutputConversion
-    from ._output_conversion_image import (
-        OutputConversionImageToColumn,
-        OutputConversionImageToImage,
-        OutputConversionImageToTable,
-    )
-    from ._output_conversion_table import OutputConversionTable
-    from ._output_conversion_time_series import OutputConversionTimeSeries
-    from ._pooling2d_layer import AvgPooling2DLayer, MaxPooling2DLayer
 
 apipkg.initpkg(
     __name__,
     {
-        "AvgPooling2DLayer": "._pooling2d_layer:AvgPooling2DLayer",
-        "Convolutional2DLayer": "._convolutional2d_layer:Convolutional2DLayer",
-        "ConvolutionalTranspose2DLayer": "._convolutional2d_layer:ConvolutionalTranspose2DLayer",
-        "FlattenLayer": "._flatten_layer:FlattenLayer",
-        "ForwardLayer": "._forward_layer:ForwardLayer",
-        "InputConversion": "._input_conversion:InputConversion",
-        "InputConversionImage": "._input_conversion_image:InputConversionImage",
-        "InputConversionTable": "._input_conversion_table:InputConversionTable",
-        "Layer": "._layer:Layer",
-        "OutputConversion": "._output_conversion:OutputConversion",
-        "InputConversionTimeSeries": "._input_conversion_time_series:InputConversionTimeSeries",
-        "LSTMLayer": "._lstm_layer:LSTMLayer",
-        "OutputConversionTable": "._output_conversion_table:OutputConversionTable",
-        "OutputConversionTimeSeries": "._output_conversion_time_series:OutputConversionTimeSeries",
-        "MaxPooling2DLayer": "._pooling2d_layer:MaxPooling2DLayer",
         "NeuralNetworkClassifier": "._model:NeuralNetworkClassifier",
         "NeuralNetworkRegressor": "._model:NeuralNetworkRegressor",
-        "OutputConversionImageToColumn": "._output_conversion_image:OutputConversionImageToColumn",
-        "OutputConversionImageToImage": "._output_conversion_image:OutputConversionImageToImage",
-        "OutputConversionImageToTable": "._output_conversion_image:OutputConversionImageToTable",
     },
 )
 
 __all__ = [
-    "AvgPooling2DLayer",
-    "Convolutional2DLayer",
-    "ConvolutionalTranspose2DLayer",
-    "FlattenLayer",
-    "ForwardLayer",
-    "InputConversion",
-    "InputConversionImage",
-    "InputConversionTable",
-    "Layer",
-    "MaxPooling2DLayer",
-    "OutputConversion",
-    "InputConversionTimeSeries",
-    "LSTMLayer",
-    "OutputConversionTable",
-    "OutputConversionTimeSeries",
     "NeuralNetworkClassifier",
     "NeuralNetworkRegressor",
-    "OutputConversionImageToColumn",
-    "OutputConversionImageToImage",
-    "OutputConversionImageToTable",
 ]

src/safeds/ml/nn/_model.py

@@ -14,25 +14,28 @@
     InvalidModelStructureError,
     ModelNotFittedError,
 )
-from safeds.ml.nn import (
-    Convolutional2DLayer,
-    FlattenLayer,
-    ForwardLayer,
+from safeds.ml.nn.converters import (
     InputConversionImage,
     OutputConversionImageToColumn,
     OutputConversionImageToImage,
     OutputConversionImageToTable,
 )
-from safeds.ml.nn._output_conversion_image import _OutputConversionImage
-from safeds.ml.nn._pooling2d_layer import _Pooling2DLayer
+from safeds.ml.nn.converters._output_conversion_image import _OutputConversionImage
+from safeds.ml.nn.layers import (
+    Convolutional2DLayer,
+    FlattenLayer,
+    ForwardLayer,
+)
+from safeds.ml.nn.layers._pooling2d_layer import _Pooling2DLayer
 
 if TYPE_CHECKING:
     from collections.abc import Callable
 
     from torch import Tensor, nn
 
     from safeds.data.image.typing import ImageSize
-    from safeds.ml.nn import InputConversion, Layer, OutputConversion
+    from safeds.ml.nn.converters import InputConversion, OutputConversion
+    from safeds.ml.nn.layers import Layer
 
 
 IFT = TypeVar("IFT", TabularDataset, TimeSeriesDataset, ImageDataset)  # InputFitType

src/safeds/ml/nn/converters/__init__.py

@@ -0,0 +1,48 @@
+"""Converters between our data contains and tensors."""
+
+from typing import TYPE_CHECKING
+
+import apipkg
+
+if TYPE_CHECKING:
+    from ._input_conversion import InputConversion
+    from ._input_conversion_image import InputConversionImage
+    from ._input_conversion_table import InputConversionTable
+    from ._input_conversion_time_series import InputConversionTimeSeries
+    from ._output_conversion import OutputConversion
+    from ._output_conversion_image import (
+        OutputConversionImageToColumn,
+        OutputConversionImageToImage,
+        OutputConversionImageToTable,
+    )
+    from ._output_conversion_table import OutputConversionTable
+    from ._output_conversion_time_series import OutputConversionTimeSeries
+
+apipkg.initpkg(
+    __name__,
+    {
+        "InputConversion": "._input_conversion:InputConversion",
+        "InputConversionImage": "._input_conversion_image:InputConversionImage",
+        "InputConversionTable": "._input_conversion_table:InputConversionTable",
+        "InputConversionTimeSeries": "._input_conversion_time_series:InputConversionTimeSeries",
+        "OutputConversion": "._output_conversion:OutputConversion",
+        "OutputConversionImageToColumn": "._output_conversion_image:OutputConversionImageToColumn",
+        "OutputConversionImageToImage": "._output_conversion_image:OutputConversionImageToImage",
+        "OutputConversionImageToTable": "._output_conversion_image:OutputConversionImageToTable",
+        "OutputConversionTable": "._output_conversion_table:OutputConversionTable",
+        "OutputConversionTimeSeries": "._output_conversion_time_series:OutputConversionTimeSeries",
+    },
+)
+
+__all__ = [
+    "InputConversion",
+    "InputConversionImage",
+    "InputConversionTable",
+    "InputConversionTimeSeries",
+    "OutputConversion",
+    "OutputConversionImageToColumn",
+    "OutputConversionImageToImage",
+    "OutputConversionImageToTable",
+    "OutputConversionTable",
+    "OutputConversionTimeSeries",
+]

src/safeds/ml/nn/_input_conversion.py → src/safeds/ml/nn/converters/_input_conversion.py

@@ -3,16 +3,16 @@
 from abc import ABC, abstractmethod
 from typing import TYPE_CHECKING, Any, Generic, TypeVar
 
-if TYPE_CHECKING:
-    from torch.utils.data import DataLoader
-
-from safeds.data.image.containers._single_size_image_list import _SingleSizeImageList
-from safeds.data.image.typing import ImageSize
-
 from safeds.data.image.containers import ImageList
 from safeds.data.labeled.containers import ImageDataset, TabularDataset, TimeSeriesDataset
 from safeds.data.tabular.containers import Table
 
+if TYPE_CHECKING:
+    from torch.utils.data import DataLoader
+
+    from safeds.data.image.containers._single_size_image_list import _SingleSizeImageList
+    from safeds.data.image.typing import ImageSize
+
 FT = TypeVar("FT", TabularDataset, TimeSeriesDataset, ImageDataset)
 PT = TypeVar("PT", Table, TimeSeriesDataset, ImageList)
 

src/safeds/ml/nn/_input_conversion_image.py → src/safeds/ml/nn/converters/_input_conversion_image.py

@@ -9,12 +9,12 @@
 from safeds.data.labeled.containers import ImageDataset
 from safeds.data.labeled.containers._image_dataset import _ColumnAsTensor, _TableAsTensor
 
+from ._input_conversion import InputConversion
+
 if TYPE_CHECKING:
     from safeds.data.image.typing import ImageSize
     from safeds.data.tabular.transformation import OneHotEncoder
 
-from safeds.ml.nn import InputConversion
-
 
 class InputConversionImage(InputConversion[ImageDataset, ImageList]):
     """The input conversion for a neural network, defines the input parameters for the neural network."""

src/safeds/ml/nn/_input_conversion_table.py → src/safeds/ml/nn/converters/_input_conversion_table.py

@@ -2,12 +2,13 @@
 
 from typing import TYPE_CHECKING, Any
 
-if TYPE_CHECKING:
-    from torch.utils.data import DataLoader
-
 from safeds.data.labeled.containers import TabularDataset
 from safeds.data.tabular.containers import Table
-from safeds.ml.nn import InputConversion
+
+from ._input_conversion import InputConversion
+
+if TYPE_CHECKING:
+    from torch.utils.data import DataLoader
 
 
 class InputConversionTable(InputConversion[TabularDataset, Table]):

src/safeds/ml/nn/_input_conversion_time_series.py → src/safeds/ml/nn/converters/_input_conversion_time_series.py

@@ -2,12 +2,13 @@
 
 from typing import TYPE_CHECKING, Any
 
+from safeds.data.labeled.containers import TimeSeriesDataset
+
+from ._input_conversion import InputConversion
+
 if TYPE_CHECKING:
     from torch.utils.data import DataLoader
 
-from safeds.data.labeled.containers import TimeSeriesDataset
-from safeds.ml.nn._input_conversion import InputConversion
-
 
 class InputConversionTimeSeries(InputConversion[TimeSeriesDataset, TimeSeriesDataset]):
     """The input conversion for a neural network, defines the input parameters for the neural network."""

src/safeds/ml/nn/_output_conversion.py → src/safeds/ml/nn/converters/_output_conversion.py

@@ -4,14 +4,12 @@
 from typing import TYPE_CHECKING, Any, Generic, TypeVar
 
 from safeds.data.image.containers import ImageList
-from safeds.data.labeled.containers import ImageDataset, TabularDataset
+from safeds.data.labeled.containers import ImageDataset, TabularDataset, TimeSeriesDataset
+from safeds.data.tabular.containers import Table
 
 if TYPE_CHECKING:
     from torch import Tensor
 
-from safeds.data.labeled.containers import TimeSeriesDataset
-from safeds.data.tabular.containers import Table
-
 IT = TypeVar("IT", Table, TimeSeriesDataset, ImageList)
 OT = TypeVar("OT", TabularDataset, TimeSeriesDataset, ImageDataset)
 

src/safeds/ml/nn/_output_conversion_image.py → src/safeds/ml/nn/converters/_output_conversion_image.py

@@ -10,13 +10,13 @@
 from safeds.data.labeled.containers import ImageDataset
 from safeds.data.labeled.containers._image_dataset import _ColumnAsTensor, _TableAsTensor
 from safeds.data.tabular.containers import Column, Table
+from safeds.data.tabular.transformation import OneHotEncoder
+
+from ._output_conversion import OutputConversion
 
 if TYPE_CHECKING:
     from torch import Tensor
 
-from safeds.data.tabular.transformation import OneHotEncoder
-from safeds.ml.nn import OutputConversion
-
 
 class _OutputConversionImage(OutputConversion[ImageList, ImageDataset], ABC):
 

src/safeds/ml/nn/_output_conversion_table.py → src/safeds/ml/nn/converters/_output_conversion_table.py

@@ -2,12 +2,13 @@
 
 from typing import TYPE_CHECKING, Any
 
-if TYPE_CHECKING:
-    from torch import Tensor
-
 from safeds.data.labeled.containers import TabularDataset
 from safeds.data.tabular.containers import Column, Table
-from safeds.ml.nn import OutputConversion
+
+from ._output_conversion import OutputConversion
+
+if TYPE_CHECKING:
+    from torch import Tensor
 
 
 class OutputConversionTable(OutputConversion[Table, TabularDataset]):

src/safeds/ml/nn/_output_conversion_time_series.py → src/safeds/ml/nn/converters/_output_conversion_time_series.py

@@ -4,12 +4,13 @@
 from typing import TYPE_CHECKING, Any
 
 from safeds._utils import _structural_hash
+from safeds.data.labeled.containers import TimeSeriesDataset
+from safeds.data.tabular.containers import Column
+
+from ._output_conversion import OutputConversion
 
 if TYPE_CHECKING:
     from torch import Tensor
-from safeds.data.labeled.containers import TimeSeriesDataset
-from safeds.data.tabular.containers import Column
-from safeds.ml.nn._output_conversion import OutputConversion
 
 
 class OutputConversionTimeSeries(OutputConversion[TimeSeriesDataset, TimeSeriesDataset]):

src/safeds/ml/nn/layers/__init__.py

@@ -0,0 +1,38 @@
+"""Layers of neural networks."""
+
+from typing import TYPE_CHECKING
+
+import apipkg
+
+if TYPE_CHECKING:
+    from ._convolutional2d_layer import Convolutional2DLayer, ConvolutionalTranspose2DLayer
+    from ._flatten_layer import FlattenLayer
+    from ._forward_layer import ForwardLayer
+    from ._layer import Layer
+    from ._lstm_layer import LSTMLayer
+    from ._pooling2d_layer import AveragePooling2DLayer, MaxPooling2DLayer
+
+apipkg.initpkg(
+    __name__,
+    {
+        "Convolutional2DLayer": "._convolutional2d_layer:Convolutional2DLayer",
+        "ConvolutionalTranspose2DLayer": "._convolutional2d_layer:ConvolutionalTranspose2DLayer",
+        "FlattenLayer": "._flatten_layer:FlattenLayer",
+        "ForwardLayer": "._forward_layer:ForwardLayer",
+        "Layer": "._layer:Layer",
+        "LSTMLayer": "._lstm_layer:LSTMLayer",
+        "AveragePooling2DLayer": "._pooling2d_layer:AveragePooling2DLayer",
+        "MaxPooling2DLayer": "._pooling2d_layer:MaxPooling2DLayer",
+    },
+)
+
+__all__ = [
+    "Convolutional2DLayer",
+    "ConvolutionalTranspose2DLayer",
+    "FlattenLayer",
+    "ForwardLayer",
+    "Layer",
+    "LSTMLayer",
+    "AveragePooling2DLayer",
+    "MaxPooling2DLayer",
+]

src/safeds/ml/nn/_convolutional2d_layer.py → src/safeds/ml/nn/layers/_convolutional2d_layer.py

@@ -8,11 +8,11 @@
 from safeds._utils import _structural_hash
 from safeds.data.image.typing import ImageSize
 
+from ._layer import Layer
+
 if TYPE_CHECKING:
     from torch import Tensor, nn
 
-from safeds.ml.nn import Layer
-
 
 def _create_internal_model(
     input_size: int,

src/safeds/ml/nn/_flatten_layer.py → src/safeds/ml/nn/layers/_flatten_layer.py

@@ -6,13 +6,13 @@
 from safeds._config import _init_default_device
 from safeds._utils import _structural_hash
 
+from ._layer import Layer
+
 if TYPE_CHECKING:
     from torch import Tensor, nn
 
     from safeds.data.image.typing import ImageSize
 
-from safeds.ml.nn import Layer
-
 
 def _create_internal_model() -> nn.Module:
     from torch import nn

src/safeds/ml/nn/_forward_layer.py → src/safeds/ml/nn/layers/_forward_layer.py

@@ -3,15 +3,15 @@
 from typing import TYPE_CHECKING, Any
 
 from safeds._config import _init_default_device
+from safeds._utils import _structural_hash
 from safeds._validation import _check_bounds, _ClosedBound
 from safeds.data.image.typing import ImageSize
 
+from ._layer import Layer
+
 if TYPE_CHECKING:
     from torch import Tensor, nn
 
-from safeds._utils import _structural_hash
-from safeds.ml.nn import Layer
-
 
 def _create_internal_model(input_size: int, output_size: int, activation_function: str) -> nn.Module:
     from torch import nn