I am aware that, while employing `loss.backward()`

we need to specify `retain_graph=True`

if there are multiple networks and multiple loss functions to optimize each network separately. But even with (or without) specifying this parameter I am getting errors. Following is an MWE to reproduce the issue (on PyTorch 1.6).

```
import torch
from torch import nn
from torch import optim
torch.autograd.set_detect_anomaly(True)
class GRU1(nn.Module):
def __init__(self):
super(GRU1, self).__init__()
self.brnn = nn.GRU(input_size=2, bidirectional=True, num_layers=1, hidden_size=100)
def forward(self, x):
return self.brnn(x)
class GRU2(nn.Module):
def __init__(self):
super(GRU2, self).__init__()
self.brnn = nn.GRU(input_size=200, bidirectional=True, num_layers=1, hidden_size=1)
def forward(self, x):
return self.brnn(x)
for i in range(100):
gru1 = GRU1()
gru2 = GRU2()
gru1_opt = optim.Adam(gru1.parameters())
gru2_opt = optim.Adam(gru2.parameters())
gru1_opt.zero_grad()
gru2_opt.zero_grad()
criterion = nn.MSELoss()
vector = torch.randn((15, 100, 2))
gru1_output, _ = gru1(vector) # (15, 100, 200)
loss_gru1 = criterion(gru1_output, torch.randn((15, 100, 200)))
loss_gru1.backward(retain_graph=True)
gru1_opt.step()
gru2_output, _ = gru2(gru1_output) # (15, 100, 2)
loss_gru2 = criterion(gru2_output, torch.randn((15, 100, 2)))
loss_gru2.backward(retain_graph=True)
gru2_opt.step()
print(f"GRU1 loss: {loss_gru1.item()}, GRU2 loss: {loss_gru2.item()}")
```

With `retain_graph`

set to `True`

I get the error

```
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor [100, 300]], which is output 0 of TBackward, is at version 2; expected version 1 instead. Hint: the backtrace further above shows the operation that failed to compute its gradient. The variable in question was changed in there or anywhere later. Good luck!
```

The error without the parameter is

```
RuntimeError: Trying to backward through the graph a second time, but the saved intermediate results have already been freed. Specify retain_graph=True when calling backward the first time.
```

which is expected.

Please point at what needs to be changed in the above code for it to begin training. Any help would be appreciated.