You could either replace the first layer in GoogleNet with e.g. an nn.Sequential container containing the new conv layer as well as the pretrained one:
model = models.googlenet()
model.conv1 = nn.Sequential(
    nn.Conv2d(1, 3, 1),
    model.conv1)

x = torch.randn(1, 1, 224, 224)
out = model(x)

or just use an extra layer before the model:
conv = nn.Conv2d(1, 3, 1)
model = models.googlenet()

x = torch.randn(1, 1, 224, 224)
out = conv(x)
out = model(out)