We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Did cifar10 dataset need gaussian blur
In the https://github.com/lightly-ai/lightly/blob/master/lightly/data/collate.py,  class SimCLRCollateFunction() presents a n example for using it,
collate_fn = SimCLRCollateFunction(
        >>>     input_size=32,
        >>>     gaussian_blur=0.,
        >>> )
but in https://docs.lightly.ai/examples/simclr.html
collate_fn = SimCLRCollateFunction(input_size=32)
so I wonder which one is the one you suggested?

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `examples/pytorch/simclr.py`
Content:
```
1 import torch
2 from torch import nn
3 import torchvision
4 
5 from lightly.data import LightlyDataset
6 from lightly.data import SimCLRCollateFunction
7 from lightly.loss import NTXentLoss
8 from lightly.models.modules import SimCLRProjectionHead
9 
10 
11 class SimCLR(nn.Module):
12     def __init__(self, backbone):
13         super().__init__()
14         self.backbone = backbone
15         self.projection_head = SimCLRProjectionHead(512, 512, 128)
16 
17     def forward(self, x):
18         x = self.backbone(x).flatten(start_dim=1)
19         z = self.projection_head(x)
20         return z
21 
22 
23 resnet = torchvision.models.resnet18()
24 backbone = nn.Sequential(*list(resnet.children())[:-1])
25 model = SimCLR(backbone)
26 
27 device = "cuda" if torch.cuda.is_available() else "cpu"
28 model.to(device)
29 
30 cifar10 = torchvision.datasets.CIFAR10("datasets/cifar10", download=True)
31 dataset = LightlyDataset.from_torch_dataset(cifar10)
32 # or create a dataset from a folder containing images or videos:
33 # dataset = LightlyDataset("path/to/folder")
34 
35 collate_fn = SimCLRCollateFunction(input_size=32)
36 
37 dataloader = torch.utils.data.DataLoader(
38     dataset,
39     batch_size=256,
40     collate_fn=collate_fn,
41     shuffle=True,
42     drop_last=True,
43     num_workers=8,
44 )
45 
46 criterion = NTXentLoss()
47 optimizer = torch.optim.SGD(model.parameters(), lr=0.06)
48 
49 print("Starting Training")
50 for epoch in range(10):
51     total_loss = 0
52     for (x0, x1), _, _ in dataloader:
53         x0 = x0.to(device)
54         x1 = x1.to(device)
55         z0 = model(x0)
56         z1 = model(x1)
57         loss = criterion(z0, z1)
58         total_loss += loss.detach()
59         loss.backward()
60         optimizer.step()
61         optimizer.zero_grad()
62     avg_loss = total_loss / len(dataloader)
63     print(f"epoch: {epoch:>02}, loss: {avg_loss:.5f}")
64
```

Path: `examples/pytorch_lightning/simclr.py`
Content:
```
1 import torch
2 from torch import nn
3 import torchvision
4 import pytorch_lightning as pl
5 
6 from lightly.data import LightlyDataset
7 from lightly.data import SimCLRCollateFunction
8 from lightly.loss import NTXentLoss
9 from lightly.models.modules import SimCLRProjectionHead
10 
11 
12 class SimCLR(pl.LightningModule):
13     def __init__(self):
14         super().__init__()
15         resnet = torchvision.models.resnet18()
16         self.backbone = nn.Sequential(*list(resnet.children())[:-1])
17         self.projection_head = SimCLRProjectionHead(512, 2048, 2048)
18         self.criterion = NTXentLoss()
19 
20     def forward(self, x):
21         x = self.backbone(x).flatten(start_dim=1)
22         z = self.projection_head(x)
23         return z
24 
25     def training_step(self, batch, batch_index):
26         (x0, x1), _, _ = batch
27         z0 = self.forward(x0)
28         z1 = self.forward(x1)
29         loss = self.criterion(z0, z1)
30         return loss
31 
32     def configure_optimizers(self):
33         optim = torch.optim.SGD(self.parameters(), lr=0.06)
34         return optim
35 
36 
37 model = SimCLR()
38 
39 cifar10 = torchvision.datasets.CIFAR10("datasets/cifar10", download=True)
40 dataset = LightlyDataset.from_torch_dataset(cifar10)
41 # or create a dataset from a folder containing images or videos:
42 # dataset = LightlyDataset("path/to/folder")
43 
44 collate_fn = SimCLRCollateFunction(input_size=32)
45 
46 dataloader = torch.utils.data.DataLoader(
47     dataset,
48     batch_size=256,
49     collate_fn=collate_fn,
50     shuffle=True,
51     drop_last=True,
52     num_workers=8,
53 )
54 
55 gpus = 1 if torch.cuda.is_available() else 0
56 
57 trainer = pl.Trainer(max_epochs=10, gpus=gpus)
58 trainer.fit(model=model, train_dataloaders=dataloader)
59
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/examples/pytorch/simclr.py b/examples/pytorch/simclr.py
--- a/examples/pytorch/simclr.py
+++ b/examples/pytorch/simclr.py
@@ -32,7 +32,10 @@
 # or create a dataset from a folder containing images or videos:
 # dataset = LightlyDataset("path/to/folder")
 
-collate_fn = SimCLRCollateFunction(input_size=32)
+collate_fn = SimCLRCollateFunction(
+    input_size=32,
+    gaussian_blur=0.,
+)
 
 dataloader = torch.utils.data.DataLoader(
     dataset,
diff --git a/examples/pytorch_lightning/simclr.py b/examples/pytorch_lightning/simclr.py
--- a/examples/pytorch_lightning/simclr.py
+++ b/examples/pytorch_lightning/simclr.py
@@ -41,7 +41,10 @@
 # or create a dataset from a folder containing images or videos:
 # dataset = LightlyDataset("path/to/folder")
 
-collate_fn = SimCLRCollateFunction(input_size=32)
+collate_fn = SimCLRCollateFunction(
+    input_size=32,
+    gaussian_blur=0.,
+)
 
 dataloader = torch.utils.data.DataLoader(
     dataset,

{"golden_diff": "diff --git a/examples/pytorch/simclr.py b/examples/pytorch/simclr.py\n--- a/examples/pytorch/simclr.py\n+++ b/examples/pytorch/simclr.py\n@@ -32,7 +32,10 @@\n # or create a dataset from a folder containing images or videos:\n # dataset = LightlyDataset(\"path/to/folder\")\n \n-collate_fn = SimCLRCollateFunction(input_size=32)\n+collate_fn = SimCLRCollateFunction(\n+    input_size=32,\n+    gaussian_blur=0.,\n+)\n \n dataloader = torch.utils.data.DataLoader(\n     dataset,\ndiff --git a/examples/pytorch_lightning/simclr.py b/examples/pytorch_lightning/simclr.py\n--- a/examples/pytorch_lightning/simclr.py\n+++ b/examples/pytorch_lightning/simclr.py\n@@ -41,7 +41,10 @@\n # or create a dataset from a folder containing images or videos:\n # dataset = LightlyDataset(\"path/to/folder\")\n \n-collate_fn = SimCLRCollateFunction(input_size=32)\n+collate_fn = SimCLRCollateFunction(\n+    input_size=32,\n+    gaussian_blur=0.,\n+)\n \n dataloader = torch.utils.data.DataLoader(\n     dataset,\n", "issue": "Did cifar10 dataset need gaussian blur\nIn the https://github.com/lightly-ai/lightly/blob/master/lightly/data/collate.py,  class SimCLRCollateFunction() presents a n example for using it,\r\ncollate_fn = SimCLRCollateFunction(\r\n        >>>     input_size=32,\r\n        >>>     gaussian_blur=0.,\r\n        >>> )\r\nbut in https://docs.lightly.ai/examples/simclr.html\r\ncollate_fn = SimCLRCollateFunction(input_size=32)\r\nso I wonder which one is the one you suggested?\n", "before_files": [{"content": "import torch\nfrom torch import nn\nimport torchvision\n\nfrom lightly.data import LightlyDataset\nfrom lightly.data import SimCLRCollateFunction\nfrom lightly.loss import NTXentLoss\nfrom lightly.models.modules import SimCLRProjectionHead\n\n\nclass SimCLR(nn.Module):\n    def __init__(self, backbone):\n        super().__init__()\n        self.backbone = backbone\n        self.projection_head = SimCLRProjectionHead(512, 512, 128)\n\n    def forward(self, x):\n        x = self.backbone(x).flatten(start_dim=1)\n        z = self.projection_head(x)\n        return z\n\n\nresnet = torchvision.models.resnet18()\nbackbone = nn.Sequential(*list(resnet.children())[:-1])\nmodel = SimCLR(backbone)\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nmodel.to(device)\n\ncifar10 = torchvision.datasets.CIFAR10(\"datasets/cifar10\", download=True)\ndataset = LightlyDataset.from_torch_dataset(cifar10)\n# or create a dataset from a folder containing images or videos:\n# dataset = LightlyDataset(\"path/to/folder\")\n\ncollate_fn = SimCLRCollateFunction(input_size=32)\n\ndataloader = torch.utils.data.DataLoader(\n    dataset,\n    batch_size=256,\n    collate_fn=collate_fn,\n    shuffle=True,\n    drop_last=True,\n    num_workers=8,\n)\n\ncriterion = NTXentLoss()\noptimizer = torch.optim.SGD(model.parameters(), lr=0.06)\n\nprint(\"Starting Training\")\nfor epoch in range(10):\n    total_loss = 0\n    for (x0, x1), _, _ in dataloader:\n        x0 = x0.to(device)\n        x1 = x1.to(device)\n        z0 = model(x0)\n        z1 = model(x1)\n        loss = criterion(z0, z1)\n        total_loss += loss.detach()\n        loss.backward()\n        optimizer.step()\n        optimizer.zero_grad()\n    avg_loss = total_loss / len(dataloader)\n    print(f\"epoch: {epoch:>02}, loss: {avg_loss:.5f}\")\n", "path": "examples/pytorch/simclr.py"}, {"content": "import torch\nfrom torch import nn\nimport torchvision\nimport pytorch_lightning as pl\n\nfrom lightly.data import LightlyDataset\nfrom lightly.data import SimCLRCollateFunction\nfrom lightly.loss import NTXentLoss\nfrom lightly.models.modules import SimCLRProjectionHead\n\n\nclass SimCLR(pl.LightningModule):\n    def __init__(self):\n        super().__init__()\n        resnet = torchvision.models.resnet18()\n        self.backbone = nn.Sequential(*list(resnet.children())[:-1])\n        self.projection_head = SimCLRProjectionHead(512, 2048, 2048)\n        self.criterion = NTXentLoss()\n\n    def forward(self, x):\n        x = self.backbone(x).flatten(start_dim=1)\n        z = self.projection_head(x)\n        return z\n\n    def training_step(self, batch, batch_index):\n        (x0, x1), _, _ = batch\n        z0 = self.forward(x0)\n        z1 = self.forward(x1)\n        loss = self.criterion(z0, z1)\n        return loss\n\n    def configure_optimizers(self):\n        optim = torch.optim.SGD(self.parameters(), lr=0.06)\n        return optim\n\n\nmodel = SimCLR()\n\ncifar10 = torchvision.datasets.CIFAR10(\"datasets/cifar10\", download=True)\ndataset = LightlyDataset.from_torch_dataset(cifar10)\n# or create a dataset from a folder containing images or videos:\n# dataset = LightlyDataset(\"path/to/folder\")\n\ncollate_fn = SimCLRCollateFunction(input_size=32)\n\ndataloader = torch.utils.data.DataLoader(\n    dataset,\n    batch_size=256,\n    collate_fn=collate_fn,\n    shuffle=True,\n    drop_last=True,\n    num_workers=8,\n)\n\ngpus = 1 if torch.cuda.is_available() else 0\n\ntrainer = pl.Trainer(max_epochs=10, gpus=gpus)\ntrainer.fit(model=model, train_dataloaders=dataloader)\n", "path": "examples/pytorch_lightning/simclr.py"}], "after_files": [{"content": "import torch\nfrom torch import nn\nimport torchvision\n\nfrom lightly.data import LightlyDataset\nfrom lightly.data import SimCLRCollateFunction\nfrom lightly.loss import NTXentLoss\nfrom lightly.models.modules import SimCLRProjectionHead\n\n\nclass SimCLR(nn.Module):\n    def __init__(self, backbone):\n        super().__init__()\n        self.backbone = backbone\n        self.projection_head = SimCLRProjectionHead(512, 512, 128)\n\n    def forward(self, x):\n        x = self.backbone(x).flatten(start_dim=1)\n        z = self.projection_head(x)\n        return z\n\n\nresnet = torchvision.models.resnet18()\nbackbone = nn.Sequential(*list(resnet.children())[:-1])\nmodel = SimCLR(backbone)\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nmodel.to(device)\n\ncifar10 = torchvision.datasets.CIFAR10(\"datasets/cifar10\", download=True)\ndataset = LightlyDataset.from_torch_dataset(cifar10)\n# or create a dataset from a folder containing images or videos:\n# dataset = LightlyDataset(\"path/to/folder\")\n\ncollate_fn = SimCLRCollateFunction(\n    input_size=32,\n    gaussian_blur=0.,\n)\n\ndataloader = torch.utils.data.DataLoader(\n    dataset,\n    batch_size=256,\n    collate_fn=collate_fn,\n    shuffle=True,\n    drop_last=True,\n    num_workers=8,\n)\n\ncriterion = NTXentLoss()\noptimizer = torch.optim.SGD(model.parameters(), lr=0.06)\n\nprint(\"Starting Training\")\nfor epoch in range(10):\n    total_loss = 0\n    for (x0, x1), _, _ in dataloader:\n        x0 = x0.to(device)\n        x1 = x1.to(device)\n        z0 = model(x0)\n        z1 = model(x1)\n        loss = criterion(z0, z1)\n        total_loss += loss.detach()\n        loss.backward()\n        optimizer.step()\n        optimizer.zero_grad()\n    avg_loss = total_loss / len(dataloader)\n    print(f\"epoch: {epoch:>02}, loss: {avg_loss:.5f}\")\n", "path": "examples/pytorch/simclr.py"}, {"content": "import torch\nfrom torch import nn\nimport torchvision\nimport pytorch_lightning as pl\n\nfrom lightly.data import LightlyDataset\nfrom lightly.data import SimCLRCollateFunction\nfrom lightly.loss import NTXentLoss\nfrom lightly.models.modules import SimCLRProjectionHead\n\n\nclass SimCLR(pl.LightningModule):\n    def __init__(self):\n        super().__init__()\n        resnet = torchvision.models.resnet18()\n        self.backbone = nn.Sequential(*list(resnet.children())[:-1])\n        self.projection_head = SimCLRProjectionHead(512, 2048, 2048)\n        self.criterion = NTXentLoss()\n\n    def forward(self, x):\n        x = self.backbone(x).flatten(start_dim=1)\n        z = self.projection_head(x)\n        return z\n\n    def training_step(self, batch, batch_index):\n        (x0, x1), _, _ = batch\n        z0 = self.forward(x0)\n        z1 = self.forward(x1)\n        loss = self.criterion(z0, z1)\n        return loss\n\n    def configure_optimizers(self):\n        optim = torch.optim.SGD(self.parameters(), lr=0.06)\n        return optim\n\n\nmodel = SimCLR()\n\ncifar10 = torchvision.datasets.CIFAR10(\"datasets/cifar10\", download=True)\ndataset = LightlyDataset.from_torch_dataset(cifar10)\n# or create a dataset from a folder containing images or videos:\n# dataset = LightlyDataset(\"path/to/folder\")\n\ncollate_fn = SimCLRCollateFunction(\n    input_size=32,\n    gaussian_blur=0.,\n)\n\ndataloader = torch.utils.data.DataLoader(\n    dataset,\n    batch_size=256,\n    collate_fn=collate_fn,\n    shuffle=True,\n    drop_last=True,\n    num_workers=8,\n)\n\ngpus = 1 if torch.cuda.is_available() else 0\n\ntrainer = pl.Trainer(max_epochs=10, gpus=gpus)\ntrainer.fit(model=model, train_dataloaders=dataloader)\n", "path": "examples/pytorch_lightning/simclr.py"}]}