The `torch.utils.tensorboard.writer.SummaryWriter.add_graph`

method takes a `input_to_model`

which is "a variable or a tuple of variables to be fed, and *this tuple is expanded into multiples arguments.*

But for `torch.nn.Sequential`

models, the only way to have models that takes multiple arguments is to make models that takes only one argument (a tuple) and deconstruct it explicitly in the `forward`

code (see here).

Why aren’t both behavior consistent?

As for now, if I want to make both work together, I need to give `input_to_model=((x, y),)`

to `add_graph`

, which is not nice.

```
import torch
import torch.nn as nn
from torch.utils.tensorboard.writer import SummaryWriter
class Model_X_x(nn.Module):
def forward(self, X):
x, y = X
return x
class Model_x_xx(nn.Module):
def forward(self, x):
return x, x
x, y = torch.randn(3), torch.randn(5)
model = nn.Sequential(Model_X_x(), Model_x_xx())
pred = model((x, y))
SummaryWriter().add_graph(model, ((x, y),))
```