Hi guys. I’m trying to create multiple dataloaders using a for loop, and each of them uses a different transform. However, I notice that they always pick the transform of the last iteration.

```
train_loader = np.empty((2), dtype=np.object)
for i in range(2):
train_loader[i]= torch.utils.data.DataLoader(
torchvision.datasets.MNIST('./mnist/train/', train=True, download=True,
transform=torchvision.transforms.Compose([
torchvision.transforms.Lambda(lambda x:
torchvision.transforms.functional.affine(
img = x,
angle = 90* i,
translate = (0,0),
scale = 1,
shear = 0
)
),
torchvision.transforms.ToTensor(),
])
),
batch_size=1, shuffle=True)
```

Here I want train_loader[0] has 0 rotation and train_loader[1] has 90 degree rotation.

But if you draw pictures from these two dataloaders

```
X, y = next(iter(train_loader[0]))
plt.imshow(X[0].squeeze(), vmin=0, vmax=1)
plt.show()
X, y = next(iter(train_loader[1]))
plt.imshow(X[0].squeeze(), vmin=0, vmax=1)
plt.show()
```

they both have 90 degree rotation. Am I doing something wrong?