class_docstr: Ernie model for reading comprehension tasks (SQuAD)
class_docstr_tok: ["Ernie","model","for","reading","comprehension","tasks","(","SQuAD",")"]
docstr: Args:
      start_pos (optional, `Variable` of shape [batch_size]):
          token index of start of answer span in `context`
      end_pos (optional, `Variable` of shape [batch_size]):
          token index of end of answer span in `context`
  Returns:
      loss (`Variable` of shape []):
          Cross entropy loss mean over batch and time, ignore positions where label == -100
          if labels not set, returns None
      start_logits (`Variable` of shape [batch_size, hidden_size]):
          output logits of start position, use argmax(start_logit) to get start index
      end_logits (`Variable` of shape [batch_size, hidden_size]):
          output logits of end position, use argmax(end_logit) to get end index
docstr_tok: ["Args",":","start_pos","(","optional",",","`","Variable","`","of","shape","[","batch_size","]",")",":","token","index","of","start","of","answer","span","in","`","context","`","end_pos","(","optional",",","`","Variable","`","of","shape","[","batch_size","]",")",":","token","index","of","end","of","answer","span","in","`","context","`","Returns",":","loss","(","`","Variable","`","of","shape","[","]",")",":","Cross","entropy","loss","mean","over","batch","and","time",",","ignore","positions","where","label","==","-100","if","labels","not","set",",","returns","None","start_logits","(","`","Variable","`","of","shape","[","batch_size",",","hidden_size","]",")",":","output","logits","of","start","position",",","use","argmax","(","start_logit",")","to","get","start","index","end_logits","(","`","Variable","`","of","shape","[","batch_size",",","hidden_size","]",")",":","output","logits","of","end","position",",","use","argmax","(","end_logit",")","to","get","end","index"]
code: def forward(self, *args, **kwargs):
      """
      Args:
          start_pos (optional, `Variable` of shape [batch_size]):
              token index of start of answer span in `context`
          end_pos (optional, `Variable` of shape [batch_size]):
              token index of end of answer span in `context`
      Returns:
          loss (`Variable` of shape []):
              Cross entropy loss mean over batch and time, ignore positions where label == -100
              if labels not set, returns None
          start_logits (`Variable` of shape [batch_size, hidden_size]):
              output logits of start position, use argmax(start_logit) to get start index
          end_logits (`Variable` of shape [batch_size, hidden_size]):
              output logits of end position, use argmax(end_logit) to get end index
      """
  
      start_pos = kwargs.pop("start_pos", None)
      end_pos = kwargs.pop("end_pos", None)
      pooled, encoded = super(
          ErnieModelForQuestionAnswering, self
      ).forward(*args, **kwargs)
      encoded = self.dropout(encoded)
      encoded = self.classifier(encoded)
      start_logit, end_logits = P.unstack(encoded, axis=-1)
      if start_pos is not None and end_pos is not None:
          if len(start_pos.shape) != 1:
              start_pos = start_pos.squeeze()
          if len(end_pos.shape) != 1:
              end_pos = end_pos.squeeze()
          start_loss = F.cross_entropy(start_logit, start_pos)
          end_loss = F.cross_entropy(end_logits, end_pos)
          loss = (start_loss.mean() + end_loss.mean()) / 2.0
      else:
          loss = None
      return loss, start_logit, end_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 ErnieModelForQuestionAnswering obj's underlying member method code with example usage: obj.forward() and returns: loss,  start_logit,  end_logits