code: def forward(self, txt_inds, txt_mask):
      encoder_inputs = self.embeddings(txt_inds)
      attention_mask = txt_mask
  
      extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
      extended_attention_mask = (
          1.0 - extended_attention_mask
      ) * -10000.0
      assert not extended_attention_mask.requires_grad
      head_mask = [None] * self.config.num_hidden_layers
  
      encoder_outputs = self.encoder(
          encoder_inputs, extended_attention_mask, head_mask=head_mask
      )
      seq_output = encoder_outputs[0]
  
      return seq_output
  
imports: import functools
  import logging
  import math
  import torch
  import torch.nn.functional
  from mmf.common.registry import registry
  from mmf.models.base_model import BaseModel
  from mmf.modules.layers import ClassifierLayer
  from mmf.utils.build import build_image_encoder
  from omegaconf import OmegaConf
  from torch import nn
label_desc: Use image node_id 2 for calling the TextBert obj's underlying member method code with example usage: obj.forward(txt_inds, txt_mask) and returns: seq_output