model = MobileNetV2_L2(num_classes=len(train_dataset.classes)).cuda()

这行代码是用 PyTorch 搭建一个 MobileNetV2_L2 模型，并将其部署到 GPU 上运行。其中，num_classes 是分类任务的类别数，而 train_dataset.classes 是数据集中所有类别的列表。 MobileNetV2_L2 是一个经过改进的 MobileNetV2 模型，它采用了 L2 正则化技术来防止过拟合。

相关问题

train_dataset = LegacyPPIDataset(mode='train') n_classes = train_dataset._labels.shape[1] num_feats = train_dataset.features.shape[1]

根据代码，'LegacyPPIDataset'对象应该已经正确创建了，并且访问'features'属性是必须的，因此第一个问题可以排除。

第二个问题可能是代码中出现了其他名称为'train_dataset'的对象并且具有'features'属性，导致代码访问到了错误的对象。建议检查一下代码中是否存在类似以下的情况：

train_dataset = ...
...
train_dataset = another_dataset(...)
...
n_classes = train_dataset._labels.shape[1]
num_feats = train_dataset.features.shape[1]

如果存在这种情况，可以将第二个'='号左边的变量名修改为其他名称，避免名称冲突。

最后，如果以上两种情况都不是问题所在，需要检查一下'LegacyPPIDataset'类的代码，确认是否对'features'属性进行了修改或删除，导致无法访问。如果是这种情况，可以修改'LegacyPPIDataset'类的代码，确保'features'属性正确存在。

model = Net(num_features=train_dataset.num_features, dim=dim, num_classes=torch.max(train_dataset.y).item() + 1).to(device)

This code initializes a neural network model with the following parameters:

- `num_features`: The number of input features for the model, which is the number of columns in the input data. This is obtained from the `train_dataset.num_features` attribute.
- `dim`: The dimensionality of the model's hidden layers. This is a hyperparameter that can be set by the user.
- `num_classes`: The number of output classes for the model, which is the maximum value in the `train_dataset.y` attribute plus one. This assumes that the class labels are contiguous integers starting from zero.
- `device`: The device (e.g. CPU or GPU) on which the model will be trained and run.

The `Net` class is assumed to be defined elsewhere in the code, and it should inherit from the `nn.Module` class in PyTorch. The `to` method is used to move the model parameters to the specified device.