class_docstr: Architecture of MLM
class_docstr_tok: ["Architecture","of","MLM"]
code: def forward(self, x, label_pos):
      # transformer unit for generating mask_c
      feature_v_seq = self.MLM_SequenceModeling_mask(x, src_mask=None)
      # position embedding layer
      label_pos = paddle.to_tensor(label_pos, dtype="int64")
      pos_emb = self.pos_embedding(label_pos)
      pos_emb = self.w0_linear(paddle.unsqueeze(pos_emb, axis=2))
      pos_emb = paddle.transpose(pos_emb, perm=[0, 2, 1])
      # fusion position embedding with features V & generate mask_c
      att_map_sub = self.active(pos_emb + self.wv(feature_v_seq))
      att_map_sub = self.we(att_map_sub)  # b,256,1
      att_map_sub = paddle.transpose(att_map_sub, perm=[0, 2, 1])
      att_map_sub = self.sigmoid(att_map_sub)  # b,1,256
      # WCL
      ## generate inputs for WCL
      att_map_sub = paddle.transpose(att_map_sub, perm=[0, 2, 1])
      f_res = x * (1 - att_map_sub)  # second path with remaining string
      f_sub = x * att_map_sub  # first path with occluded character
      ## transformer units in WCL
      f_res = self.MLM_SequenceModeling_WCL(f_res, src_mask=None)
      f_sub = self.MLM_SequenceModeling_WCL(f_sub, src_mask=None)
      return f_res, f_sub, att_map_sub
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  from paddle import ParamAttr
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn.initializer import Normal, XavierNormal
  import numpy
label_desc: Use image node_id 2 for calling the MLM obj's underlying member method code with example usage: obj.forward(x, label_pos) and returns: f_res,  f_sub,  att_map_sub