How to input Concatenation

enter image description hereenter image description hereI want to combine two models for my training data, but I do not know why it's always wrong. My topic is get one embedding, then adjust the length, combine two embedding in other def() function

# Please comment your code

    import numpy as np
    def Concatenation (posts, model):
      emb_table = []
      for i in posts:
        for word in i:
          if word in model:
            emb_table.append(model.wv[word])
          else:
            emb_table.append([0]*model.vector_size)
      return emb_table
        len_list = [len(s) for s in traning_post_new]
    seq_length = max(len_list)
    seq_length
    
    # # Padding and encoding
    # def encode_and_add_padding(sentences, seq_length, word_index):
    #     sent_encoded = []
    #     for sent in sentences:
    #         temp_encoded = [word_index[word] if word in word_index else word_index['[UNKNOWN]'] for word in sent]
    #         if len(temp_encoded) < seq_length:
    #             temp_encoded += [word_index['[PAD]']] * (seq_length - len(temp_encoded))
    #         sent_encoded.append(temp_encoded)
    #     return sent_encoded
    
    # sent_encoded = encode_and_add_padding(sentences, seq_length, word_index)
    # print(sent_encoded)
    def encode_and_add_padding(posts, model1, model2):
      for i in range(len(posts)):    # i is index
        if len(posts[i]) < seq_length:
          posts[i].extend(["[PAD]"]*(seq_length-len(posts[i]))) 
        else:
          posts[i] = posts[i][:seq_length]
      
     
      embedding1 = Concatenation (posts, model1)
      embedding2 = Concatenation (posts, model2)
      print(embedding1.shape)
      final_embedding = np.concatenate((embedding1,embedding2), 0)
      return final_embedding