For example:
import torch

hidden_dim1 = 10
hidden_dim2 = 5
tagset_size = 2

class MyModel(torch.nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.line1 = torch.nn.Linear(hidden_dim1, hidden_dim2)
        self.line2 = torch.nn.Linear(hidden_dim2, tagset_size)

    def forward(self, x, y):
        out1 = self.line1(x)
        out2 = self.line2(y)
        return out1

X = torch.randn(20, hidden_dim1)
Y = torch.randn(hidden_dim1, hidden_dim2)
inputs = (X, Y)

model = MyModel()
f = './model.onnx'
torch.onnx.export(model, inputs, f,
                    opset_version=9,
                    example_outputs=None,
                    input_names=["X"], output_names=["Y"],verbose=True)

graph(%X : Float(20, 10, strides=[10, 1], requires_grad=0, device=cpu),
      %line1.weight : Float(5, 10, strides=[10, 1], requires_grad=1, device=cpu),
      %line1.bias : Float(5, strides=[1], requires_grad=1, device=cpu)):
  %Y : Float(20, 5, strides=[5, 1], requires_grad=1, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%X, %line1.weight, %line1.bias) # /root/.conda/envs/torch1.9/lib/python3.6/site-packages/torch/nn/functional.py:1847:0
  return (%Y)


How every, the exported graph doesn’t contain line2 , maybe because the  output of MyModel  is not depend on out2 = self.line2(y) ? I guess the graph is pruned by default.
What should I do if I want to not do pruning?

Motivation
I want to do something for self.named_parameters() in model.forward(), eg.
def check_parameters():
  # do something for parameters by calling 
  # some ops including OP1, OP2 and so on
  return

class MyModel(torch.nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.line = torch.nn.Linear(hidden_dim1, hidden_dim2)

    def forward(self, x, y):
        out = self.line1(x)
        check_parameters()
        return out

How every, the exported graph doesn’t contain OP1, OP2 , maybe because the  output of MyModel  is not depend on check_parameters() ? I guess the graph is pruned by default.