class_docstr: Ernie Model for Masked Languate Model pretrain
class_docstr_tok: ["Ernie","Model","for","Masked","Languate","Model","pretrain"]
code: def __init__(self, cfg, name=None):
      super(ErnieModelForPretraining, self).__init__(cfg, name=name)
      initializer = nn.initializer.TruncatedNormal(
          std=cfg["initializer_range"]
      )
      d_model = cfg["hidden_size"]
      d_vocab = cfg["vocab_size"]
  
      self.pooler_heads = nn.LayerList([NSPHead(cfg, name=name)])
      self.mlm = _build_linear(
          d_model,
          d_model,
          append_name(name, "mask_lm_trans_fc"),
          initializer,
      )
      self.act = ACT_DICT[cfg["hidden_act"]]()
      self.mlm_ln = _build_ln(
          d_model, name=append_name(name, "mask_lm_trans")
      )
      self.mlm_bias = P.create_parameter(
          dtype="float32",
          shape=[d_vocab],
          attr=P.ParamAttr(
              name=append_name(name, "mask_lm_out_fc.b_0"),
              initializer=nn.initializer.Constant(value=0.0),
          ),
          is_bias=True,
      )
      self.train()
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import json
  import logging
  import math
  import six
  import numpy
  import paddle
  from paddle import nn
  from paddle.nn import functional
  from disco_diffusion_ernievil_base.vit_b_16x.ernievil2.transformers.file_utils import _fetch_from_remote, add_docstring
label_desc: Use image node_id 1 to create a new ErnieModelForPretraining object from inherited base classes: ErnieModel with example: obj = ErnieModelForPretraining(cfg, name)