还有个问题，可否帮助我解释这个问题：RuntimeError: torch.nn.functional.binary_cross_entropy and torch.nn.BCELoss are unsafe to autocast. Many models use a sigmoid layer right before the binary cross entropy layer. In this case, combine the two layers using torch.nn.functional.binary_cross_entropy_with_logits or torch.nn.BCEWithLogitsLoss. binary_cross_entropy_with_logits and BCEWithLogits are safe to autocast.

这个错误是在告诉你，使用`torch.nn.functional.binary_cross_entropy`或`torch.nn.BCELoss`计算二元交叉熵损失是不安全的。它建议你使用`torch.nn.functional.binary_cross_entropy_with_logits`或`torch.nn.BCEWithLogitsLoss`来代替。

在使用二元交叉熵损失的时候，通常需要在计算交叉熵损失之前使用sigmoid函数将输出转化为概率值。`binary_cross_entropy_with_logits`和`BCEWithLogitsLoss`已经内置了sigmoid函数，所以你可以直接使用它们而不用担心sigmoid函数带来的问题。

举个例子，你可以将如下代码：

import torch.nn as nn

# Compute the loss using the sigmoid of the output and the binary cross entropy loss
output = model(input)
loss = nn.functional.binary_cross_entropy(nn.functional.sigmoid(output), target)

改为如下代码：

import torch.nn as nn

# Compute the loss using the binary cross entropy loss with logits
output = model(input)
loss = nn.BCEWithLogitsLoss(output, target)