class_docstr: Ernie Model for text classfication or pointwise ranking tasks
class_docstr_tok: ["Ernie","Model","for","text","classfication","or","pointwise","ranking","tasks"]
docstr: Args:
      labels (optional, `Variable` of shape [batch_size]):
          ground truth label id for each sentence
  Returns:
      loss (`Variable` of shape []):
          Cross entropy loss mean over batch
          if labels not set, returns None
      logits (`Variable` of shape [batch_size, hidden_size]):
          output logits of classifier
docstr_tok: ["Args",":","labels","(","optional",",","`","Variable","`","of","shape","[","batch_size","]",")",":","ground","truth","label","id","for","each","sentence","Returns",":","loss","(","`","Variable","`","of","shape","[","]",")",":","Cross","entropy","loss","mean","over","batch","if","labels","not","set",",","returns","None","logits","(","`","Variable","`","of","shape","[","batch_size",",","hidden_size","]",")",":","output","logits","of","classifier"]
code: def forward(self, *args, **kwargs):
      """
      Args:
          labels (optional, `Variable` of shape [batch_size]):
              ground truth label id for each sentence
      Returns:
          loss (`Variable` of shape []):
              Cross entropy loss mean over batch
              if labels not set, returns None
          logits (`Variable` of shape [batch_size, hidden_size]):
              output logits of classifier
      """
      labels = kwargs.pop("labels", None)
      pooled, encoded = super(
          ErnieModelForSequenceClassification, self
      ).forward(*args, **kwargs)
      hidden = self.dropout(pooled)
      logits = self.classifier(hidden)
  
      if labels is not None:
          if len(labels.shape) != 1:
              labels = labels.squeeze()
          loss = F.cross_entropy(logits, labels)
      else:
          loss = None
      return loss, logits
  
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 2 for calling the ErnieModelForSequenceClassification obj's underlying member method code with example usage: obj.forward() and returns: loss,  logits