How initialize weights of a `torch.nn.Transformer` module?

I am using a vanilla transformer architecture from the "Attention Is All You Need" paper for a sequence-to-sequence task. As shown in the following code. Assuming that I would like to use the torch.nn.init.kaiming_uniform_ initialization method, how would one go about initializing the weights of the nn.Transformer ? Is it necessary to use a custom encoder and decoder class in order for that to happen?

import torch
import torch.nn as nn
import math

# helper Module that adds positional encoding to the token embedding to introduce a notion of word order.
import torch.nn as nn
class PositionalEncoding(nn.Module):
    def __init__(self,
                 emb_size: int,
                 dropout: float,
                 maxlen: int = 20):
        super(PositionalEncoding, self).__init__()

        den = torch.exp(- torch.arange(0, emb_size, 2)* math.log(10000) / emb_size)
        pos = torch.arange(0, maxlen).reshape(maxlen, 1)
        pos_embedding = torch.zeros((maxlen, emb_size))
        pos_embedding[:, 0::2] = torch.sin(pos * den)
        pos_embedding[:, 1::2] = torch.cos(pos * den)
        pos_embedding = pos_embedding.unsqueeze(-2)

        self.dropout = nn.Dropout(dropout)
        self.register_buffer('pos_embedding', pos_embedding)

    def forward(self, token_embedding: Tensor):
        return self.dropout(token_embedding + self.pos_embedding[:token_embedding.size(0), :])

# helper Module to convert tensor of input indices into corresponding tensor of token embeddings
class TokenEmbedding(nn.Module):
    def __init__(self, vocab_size: int, emb_size):
        super(TokenEmbedding, self).__init__()
        self.embedding = nn.Embedding(vocab_size, emb_size)
        # Initialize weights with He initialization
        self.embedding.weight = nn.init.kaiming_uniform_(self.embedding.weight)
        self.emb_size = emb_size

    def forward(self, tokens: Tensor):
        return self.embedding(tokens.long()) * math.sqrt(self.emb_size)

# Seq2Seq Network 
class Transformer(nn.Module):
    def __init__(self,
                 src_vocab_size: int,
                 tgt_vocab_size: int,
                 num_encoder_layers: int = 1,
                 num_decoder_layers: int = 1,
                 emb_size: int = 300,
                 nhead: int = 3,
                 dim_feedforward: int = 512,
                 dropout: float = 0.1,
                 activation_function='relu'):
        super().__init__()
        self.src_tok_emb = TokenEmbedding(src_vocab_size, emb_size)
        self.tgt_tok_emb = TokenEmbedding(tgt_vocab_size, emb_size)
        self.positional_encoding = PositionalEncoding(
            emb_size, dropout=dropout)
        
        self.transformer = torch.nn.Transformer(d_model=emb_size,
                                       nhead=nhead,
                                       num_encoder_layers=num_encoder_layers,
                                       num_decoder_layers=num_decoder_layers,
                                       dim_feedforward=dim_feedforward,
                                       dropout=dropout,
                                       batch_first=True,
                                       activation=activation_function)
        self.generator = nn.Linear(emb_size, tgt_vocab_size)
    
    def init_weights(self):
      self.generator.weight = nn.init.kaiming_uniform_(self.generator.weight)
      
    def forward(self,
                src: Tensor,
                trg: Tensor,
                src_mask: Tensor,
                tgt_mask: Tensor,
                src_padding_mask: Tensor,
                tgt_padding_mask: Tensor,
                memory_key_padding_mask: Tensor):
        # the .permute() is necessary since the positional-encoder expects tensors to be of shape
        # (seq_len, batch_size, emb_length)
        src_emb = self.positional_encoding(self.src_tok_emb(src).permute(1,0,2)).permute(1,0,2)
        tgt_emb = self.positional_encoding(self.tgt_tok_emb(trg).permute(1,0,2)).permute(1,0,2)
        outs = self.transformer(src_emb, tgt_emb, src_mask, tgt_mask, None, 
                                src_padding_mask, tgt_padding_mask, memory_key_padding_mask)
        return self.generator(outs)

    def encode(self, src: Tensor, src_mask: Tensor):
        return self.transformer.encoder(self.positional_encoding(
                            self.src_tok_emb(src)), src_mask)

    def decode(self, tgt: Tensor, memory: Tensor, tgt_mask: Tensor):
        return self.transformer.decoder(self.positional_encoding(
                          self.tgt_tok_emb(tgt)), memory,
                          tgt_mask)