style: format code

torch_frame/utils/skorch.py

@@ -1,26 +1,11 @@
-import skorch.utils
-
-# TODO: make it more safe
-old_to_tensor = skorch.utils.to_tensor
-
-def to_tensor(X, device, accept_sparse=False):
-    if isinstance(X, TensorFrame):
-        return X
-    return old_to_tensor(X, device, accept_sparse)
-
-skorch.utils.to_tensor = to_tensor
 import importlib
-importlib.reload(skorch.net)
-
 from typing import Any
 
-import pandas as pd
+import skorch.utils
 import torch
-import torch.nn as nn
 from numpy.typing import ArrayLike
 from pandas import DataFrame
-from skorch import NeuralNet, NeuralNetClassifier
-from skorch.dataset import Dataset as SkorchDataset
+from skorch import NeuralNet
 from torch import Tensor
 
 import torch_frame
@@ -29,20 +14,34 @@ def to_tensor(X, device, accept_sparse=False):
     TextEmbedderConfig,
     TextTokenizerConfig,
 )
-from torch_frame.data.dataset import DataFrameToTensorFrameConverter, Dataset
+from torch_frame.data.dataset import Dataset
 from torch_frame.data.loader import DataLoader
 from torch_frame.data.tensor_frame import TensorFrame
 from torch_frame.typing import IndexSelectType
 from torch_frame.utils import infer_df_stype
 
+# TODO: make it more safe
+old_to_tensor = skorch.utils.to_tensor
+
+
+def to_tensor(X, device, accept_sparse=False):
+    if isinstance(X, TensorFrame):
+        return X
+    return old_to_tensor(X, device, accept_sparse)
+
+
+skorch.utils.to_tensor = to_tensor
+
+importlib.reload(skorch.net)
+
 
 class NeuralNetPytorchFrameDataLoader(DataLoader):
     def __init__(self, dataset: Dataset | TensorFrame, *args,
                  device: torch.device, **kwargs):
         super().__init__(dataset, *args, **kwargs)
         self.device = device
 
-    def collate_fn(
+    def collate_fn(  # type: ignore
             self, index: IndexSelectType) -> tuple[TensorFrame, Tensor | None]:
         index = torch.tensor(index)
         res = super().collate_fn(index).to(self.device)