Model.train() and model.eval() vs model and model.eval()

4

Why do model.train() and model.eval() return a reference to the model. What is the intended usage for the return value?

I am using as follows:

model.train()

But this means that in a Jupyter notebook it outputs the model object repr which is unwanted:

LeNet(
  (m): Sequential(
    (0): Sequential(
      (0): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
      (1): BatchNorm2d(20, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
      (3): MaxPool2d(kernel_size=3, stride=3, padding=0, dilation=1, ceil_mode=False)
      (4): Dropout(p=0.25)
    )
    (1): Sequential(
      (0): Conv2d(20, 50, kernel_size=(5, 5), stride=(1, 1))
      (1): BatchNorm2d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
      (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
      (4): Dropout(p=0.25)
    )
    (2): View()
    (3): Linear(in_features=200, out_features=500, bias=True)
    (4): BatchNorm1d(500, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU(inplace)
    (6): Dropout(p=0.25)
    (7): Linear(in_features=500, out_features=10, bias=True)
  )
)