class_docstr: MMT TXT Model
  Args:
      txt_bert (nn.Module): Bert model using for text
      tokenizer (Any): Tokenizer for input text
      max_length (int): text max length
      modalities (List): modalities name list
      add_special_tokens (bool): whether use special tokens
      add_dot (bool): add dot in the end of text
      same_dim (int): same dim in the dimension of output of GatedEmbeddingUnit
      dout_prob (float): dropout prob
code: def compute_weights_from_emb(self, embd):
      embd = self.moe_txt_dropout(embd)
      moe_weights = torch.cat(
          [self.moe_fc_txt[mod](embd) for mod in self.modalities],
          dim=-1,
      )
      moe_weights = F.softmax(moe_weights, dim=1)
      return moe_weights
  
imports: import os
  import torch
  import torch.nn.functional
  from typing import Any, List, Dict
  from collections import OrderedDict
  from torch import nn
  from towhee.models.mdmmt.bert_mmt import BertMMT
label_desc: Use image node_id 3 for calling the MMTTXT obj's underlying member method code with example usage: obj.compute_weights_from_emb(embd) and returns: moe_weights