Give me an implementation of Transformer with a copy mechanism. Use Pytorch library.
Here is an implementation of a Transformer with a copy mechanism using PyTorch:
```
import torch
import torch.nn as nn
import torch.nn.functional as F

class TransformerWithCopy(nn.Module):
    def __init__(self, vocab_size, hidden_size, num_layers, dropout):
        super(TransformerWithCopy, self).__init__()
        self.encoder = TransformerEncoder(vocab_size, hidden_size, num_layers, dropout)
        self.decoder = TransformerDecoderWithCopy(vocab_size, hidden_size, num_layers, dropout)

    def forward(self, src, tgt, src_mask, tgt_mask):
        encoder_output = self.encoder(src, src_mask)
        decoder_output = self.decoder(tgt, encoder_output, src_mask, tgt_mask)
        return decoder_output

class TransformerEncoder(nn.Module):
    def __init__(self, vocab_size, hidden_size, num_layers, dropout):
        super(TransformerEncoder, self).__init__()
        self.embedding = nn.Embedding(vocab_size, hidden_size)
        self.layers = nn.ModuleList([TransformerEncoderLayer(hidden_size, dropout) for _ in range(num_layers)])

    def forward(self, src, src_mask):
        embedded_src = self.embedding(src)
        for layer in self.layers:
            embedded_src = layer(embedded_src, src_mask)
        return embedded_src

class TransformerEncoderLayer(nn.Module):
    def __init__(self, hidden_size, dropout):
        super(TransformerEncoderLayer, self).__init__()

class Transformer(torch.nn.Module):
    def __init__(self, num_layers, num_heads, vocab_size, hidden_size):
        super(Transformer, self).__init__()

        self.num_layers = num_layers
        self.num_heads = num_heads
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size

        self.layers = [TransformerLayer(num_layers, num_heads, vocab_size, hidden_size) for _ in range(num_layers)]

    def forward(self, input_tensor):
        output = self.layers[0].forward(input_tensor) 
        for layer in self.layers[1:]:
            output = layer.forward(output)
        return output

    def add_copy_mechanism(self):
        for layer in self.layers:
            layer.add_copy_mechanism()

    def get_attention_mask(self, input_tensor):
        attn_mask = torch.ones(input_tensor.shape, dtype=torch.long)
        for layer in self.layers:
            attn_mask = layer.get_attention_mask(attn_mask