## Installation
``` bash
$ pip install torch_fn
```
## Usage
``` python
from torch_fn import torch_fn
import numpy as np
import torch.nn.functional as F
@torch_fn
def torch_softmax(*args, **kwargs):
return F.softmax(*args, **kwargs)
def custom_print(x):
print(type(x), x)
# Test the decorator with different input types
x = [1, 2, 3]
x_list = x
x_tensor = torch.tensor(x).float()
x_tensor_cuda = torch.tensor(x).float().cuda()
x_array = np.array(x)
x_df = pd.DataFrame({"col1": x})
custom_print(torch_softmax(x_list, dim=-1))
# /home/ywatanabe/proj/torch_fn/src/torch_fn/_torch_fn.py:57: UserWarning: Converted from <class 'list'> to <class 'torch.Tensor'> (cuda:0)
# warnings.warn(
# <class 'numpy.ndarray'> [0.09003057 0.24472848 0.6652409 ]
custom_print(torch_softmax(x_array, dim=-1))
# /home/ywatanabe/proj/torch_fn/src/torch_fn/_torch_fn.py:57: UserWarning: Converted from <class 'numpy.ndarray'> to <class 'torch.Tensor'> (cuda:0)
# warnings.warn(
# <class 'numpy.ndarray'> [0.09003057 0.24472848 0.6652409 ]
custom_print(torch_softmax(x_df, dim=-1))
# /home/ywatanabe/proj/torch_fn/src/torch_fn/_torch_fn.py:49: UserWarning: Converted from <class 'pandas.core.frame.DataFrame'> to <class 'torch.Tensor'> (cuda:0)
# warnings.warn(
# <class 'numpy.ndarray'> [0.09003057 0.24472848 0.6652409 ]
custom_print(torch_softmax(x_tensor, dim=-1))
# <class 'torch.Tensor'> tensor([0.0900, 0.2447, 0.6652])
custom_print(torch_softmax(x_tensor_cuda, dim=-1))
# <class 'torch.Tensor'> tensor([0.0900, 0.2447, 0.6652], device='cuda:0')
```
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