Here is a short example on how to split an embedding into two parts:
import torch
import torch.nn as nn
import random

vocab_size = 10
embedding_dim_1 = 2
embedding_dim_2 = 3

embedding_1 = nn.Embedding(vocab_size, embedding_dim_1)
embedding_2 = nn.Embedding(vocab_size, embedding_dim_2)

# Random vector of length 15 consisting of indices 0, ..., 9
x = torch.LongTensor([random.randint(0, 9) for _ in range(15)])
# Adding batch dimension
x = x[None, :]

emb_1 = embedding_1(x)
print(emb_1)
emb_2 = embedding_2(x)
print(emb_2)
# Concatenating embeddings along dimension 2
emb = torch.cat([emb_1, emb_2], dim=2)
print(emb)