diff --git a/#main.py# b/#main.py#
deleted file mode 100644
index 33745a5..0000000
--- a/#main.py#
+++ /dev/null
@@ -1,63 +0,0 @@
-import os
-
-import torch
-from torch import nn
-from torchvision.datasets import MNIST
-from torchvision.transforms import ToTensor
-
-from flox import federated_fit
-from flox.federation.topologies import Topology
-from flox.learn import FloxModule
-from flox.learn.data.utils import federated_split
-
-
-class MyModule(FloxModule):
-    def __init__(self):
-        super().__init__()
-        self.flatten = nn.Flatten()
-        self.linear_stack = nn.Sequential(
-            nn.Linear(28 * 28, 512),
-            nn.ReLU(),
-            nn.Linear(512, 512),
-            nn.ReLU(),
-            nn.Linear(512, 10),
-        )
-        self.last_accuracy = torch.tensor([1.0])
-
-    def forward(self, x):
-        x = self.flatten(x)
-        logits = self.linear_stack(x)
-        return logits
-
-    def training_step(self, batch, batch_idx):
-        inputs, targets = batch
-        preds = self(inputs)
-        loss = nn.functional.cross_entropy(preds, targets)
-        return loss
-
-    def configure_optimizers(self) -> torch.optim.Optimizer:
-        return torch.optim.SGD(self.parameters(), lr=1e-3)
-
-
-def load_data():
-    return MNIST(
-        root=os.environ["TORCH_DATASETS"],
-        download=False,
-        train=False,
-        transform=ToTensor(),
-    )
-
-
-if __name__ == "__main__":
-    flock = Topology.from_yaml("examples/flocks/3-tier.yaml")
-    fed_data = federated_split(
-        load_data(),
-        flock,
-        10,
-        samples_alpha=10.0,
-        labels_alpha=10.0,
-    )
-    module, train_history = federated_fit(
-        flock, MyModule(), fed_data, 2, strategy="fedavg", launcher_kind="thread"
-    )
-    train_history.to_csv("temp.csv")
diff --git a/main.py b/main.py
deleted file mode 100644
index 33745a5..0000000
--- a/main.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import os
-
-import torch
-from torch import nn
-from torchvision.datasets import MNIST
-from torchvision.transforms import ToTensor
-
-from flox import federated_fit
-from flox.federation.topologies import Topology
-from flox.learn import FloxModule
-from flox.learn.data.utils import federated_split
-
-
-class MyModule(FloxModule):
-    def __init__(self):
-        super().__init__()
-        self.flatten = nn.Flatten()
-        self.linear_stack = nn.Sequential(
-            nn.Linear(28 * 28, 512),
-            nn.ReLU(),
-            nn.Linear(512, 512),
-            nn.ReLU(),
-            nn.Linear(512, 10),
-        )
-        self.last_accuracy = torch.tensor([1.0])
-
-    def forward(self, x):
-        x = self.flatten(x)
-        logits = self.linear_stack(x)
-        return logits
-
-    def training_step(self, batch, batch_idx):
-        inputs, targets = batch
-        preds = self(inputs)
-        loss = nn.functional.cross_entropy(preds, targets)
-        return loss
-
-    def configure_optimizers(self) -> torch.optim.Optimizer:
-        return torch.optim.SGD(self.parameters(), lr=1e-3)
-
-
-def load_data():
-    return MNIST(
-        root=os.environ["TORCH_DATASETS"],
-        download=False,
-        train=False,
-        transform=ToTensor(),
-    )
-
-
-if __name__ == "__main__":
-    flock = Topology.from_yaml("examples/flocks/3-tier.yaml")
-    fed_data = federated_split(
-        load_data(),
-        flock,
-        10,
-        samples_alpha=10.0,
-        labels_alpha=10.0,
-    )
-    module, train_history = federated_fit(
-        flock, MyModule(), fed_data, 2, strategy="fedavg", launcher_kind="thread"
-    )
-    train_history.to_csv("temp.csv")