class_docstr: Architecture of MLM
class_docstr_tok: ["Architecture","of","MLM"]
code: def __init__(self, n_dim=512, n_position=256, max_text_length=25):
      super(MLM, self).__init__()
      self.MLM_SequenceModeling_mask = Transformer_Encoder(
          n_layers=2, n_position=n_position
      )
      self.MLM_SequenceModeling_WCL = Transformer_Encoder(
          n_layers=1, n_position=n_position
      )
      self.pos_embedding = nn.Embedding(max_text_length, n_dim)
      self.w0_linear = nn.Linear(1, n_position)
      self.wv = nn.Linear(n_dim, n_dim)
      self.active = nn.Tanh()
      self.we = nn.Linear(n_dim, 1)
      self.sigmoid = nn.Sigmoid()
  
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 1 to create a new MLM object from inherited base classes: nn with example: obj = MLM(n_dim, n_position, max_text_length)