Update jupyter notebook example (#3700)

examples/notebooks/utils.py

+import os
+import torch
+import pandas as pd
+
+from sklearn.preprocessing import LabelEncoder
+from torchvision.datasets.utils import download_url
+
+class TitanicDataset(torch.utils.data.Dataset):
+    def __init__(self, root: str, train: bool = True):
+        filename = 'train.csv' if train else 'eval.csv'
+        if not os.path.exists(os.path.join(root, filename)):
+            download_url(os.path.join('https://storage.googleapis.com/tf-datasets/titanic/', filename), root, filename)
+
+        df = pd.read_csv(os.path.join(root, filename))
+        object_colunmns = df.select_dtypes(include='object').columns.values
+        for idx in df.columns:
+            if idx in object_colunmns:
+                df[idx] = LabelEncoder().fit_transform(df[idx])
+           
+        self.x = df.iloc[:, 1:].values
+        self.y = df.iloc[:, 0].values
+
+    def __len__(self):
+        return len(self.y)
+
+    def __getitem__(self, idx):
+        return torch.Tensor(self.x[idx]), self.y[idx]
+
+def accuracy(output, target):
+    batch_size = target.size(0)
+    _, predicted = torch.max(output.data, 1)
+    return {"acc1": (predicted == target).sum().item() / batch_size}
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