Repository: pytorch
Issue: changed format of trace graph in torch 1.4.0
Labels: oncall: jit,triaged

## ❓ Questions

For the following example code, the printed graph in torch 1.4.0 is very different from that in torch 1.3.1. I am wondering is this expected? because many useful fields are removed in torch 1.4.0.

```
import torch
import torch.nn as nn

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv = nn.Conv2d(1, 1, 3)

    def forward(self, x):
        return self.conv(x)

if __name__ == '__main__':
    n = Net()
    example_weight = torch.rand(1, 1, 3, 3)
    example_forward_input = torch.rand(1, 1, 3, 3)

    # Trace a specific method and construct `ScriptModule` with
    # a single `forward` method
    module = torch.jit.trace(n.forward, example_forward_input)

    # Trace a module (implicitly traces `forward`) and construct a
    # `ScriptModule` with a single `forward` method
    module = torch.jit.trace(n, example_forward_input)
    print(module.graph)
```
output in torch 1.3.1:
```
graph(%self : ClassType<Net>,
      %input : Float(1, 1, 3, 3)):
  %1 : ClassType<Conv2d> = prim::GetAttr[name="conv"](%self)
  %weight : Tensor = prim::GetAttr[name="weight"](%1)
  %3 : Tensor = prim::GetAttr[name="bias"](%1)
  %5 : int = prim::Constant[value=1](), scope: Net/Conv2d[conv] # /home/xxx/.local/lib/python3.5/site-packages/torch/nn/modules/conv.py:342:0
  %6 : int = prim::Constant[value=1](), scope: Net/Conv2d[conv] # /home/xxx/.local/lib/python3.5/site-packages/torch/nn/modules/conv.py:342:0
  %7 : int[] = prim::ListConstruct(%5, %6), scope: Net/Conv2d[conv]
  %8 : int = prim::Constant[value=0](), scope: Net/Conv2d[conv] # /home/xxx/.local/lib/python3.5/site-packages/torch/nn/modules/conv.py:342:0
  %9 : int = prim::Constant[value=0](), scope: Net/Conv2d[conv] # /home/xxx/.local/lib/python3.5/site-packages/torch/nn/modules/conv.py:342:0
  %10 : int[] = prim::ListConstruct(%8, %9), scope: Net/Conv2d[conv]
  %11 : int = prim::Constant[value=1](), scope: Net/Conv2d[conv] # /home/xxx/.local/lib/python3.5/site-packages/torch/nn/modules/conv.py:342:0
  %12 : int = prim::Constant[value=1](), scope: Net/Conv2d[conv] # /home/xxx/.local/lib/python3.5/site-packages/torch/nn/modules/conv.py:342:0
  %13 : int[] = prim::ListConstruct(%11, %12), scope: Net/Conv2d[conv]
  %14 : bool = prim::Constant[value=0](), scope: Net/Conv2d[conv] # /home/xxx/.local/lib/python3.5/site-packages/torch/nn/modules/conv.py:342:0
  %15 : int = prim::Constant[value=0](), scope: Net/Conv2d[conv] # /home/xxx/.local/lib/python3.5/site-packages/torch/nn/modules/conv.py:342:0
  %16 : int = prim::Constant[value=0](), scope: Net/Conv2d[conv] # /home/xxx/.local/lib/python3.5/site-packages/torch/nn/modules/conv.py:342:0
  %17 : int[] = prim::ListConstruct(%15, %16), scope: Net/Conv2d[conv]
  %18 : int = prim::Constant[value=1](), scope: Net/Conv2d[conv] # /home/xxx/.local/lib/python3.5/site-packages/torch/nn/modules/conv.py:342:0
  %19 : bool = prim::Constant[value=0](), scope: Net/Conv2d[conv] # /home/xxx/.local/lib/python3.5/site-packages/torch/nn/modules/conv.py:342:0
  %20 : bool = prim::Constant[value=0](), scope: Net/Conv2d[conv] # /home/xxx/.local/lib/python3.5/site-packages/torch/nn/modules/conv.py:342:0
  %21 : bool = prim::Constant[value=1](), scope: Net/Conv2d[conv] # /home/xxx/.local/lib/python3.5/site-packages/torch/nn/modules/conv.py:342:0
  %22 : Float(1, 1, 1, 1) = aten::_convolution(%input, %weight, %3, %7, %10, %13, %14, %17, %18, %19, %20, %21), scope: Net/Conv2d[conv] # /home/xxx/.local/lib/python3.5/site-packages/torch/nn/modules/conv.py:342:0
  return (%22)
```

output in torch 1.4.0:
```
graph(%self.1 : __torch__.torch.nn.modules.module.___torch_mangle_4.Module,
      %input : Float(1, 1, 3, 3)):
  %28 : __torch__.torch.nn.modules.module.___torch_mangle_3.Module = prim::GetAttr[name="conv"](%self.1)
  %30 : Tensor = prim::CallMethod[name="forward"](%28, %input)
  return (%30)
```

cc @suo