custom_embedding.py

import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor


class SharedEmbedding(nn.Embedding):

    def forward(self, input: Tensor, unembed: bool = False) -> Tensor:
        if unembed:
            return F.linear(input, self.weight)
        return super().forward(input)