The output shape of [10, 1000] looks valid for a mutli-class classification use case with 1000 classes.
I guess the target shape might be wrong.
nn.CrossEntropyLoss expects the target to have the shape [batch_size=10] containing the class indices in the range [0, nb_classes-1 = 999].
If your target is one-hot encoded, use target = torch.argmax(target, 1) to create the expected target with the class indices.