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
      end_logits (`Variable` of shape [batch_size, hidden_size]):
          output logits of end position
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code: def forward(self, inputs, labels):
      """
      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
          end_logits (`Variable` of shape [batch_size, hidden_size]):
              output logits of end position
      """
  
      logits = self.nsp(inputs)
      loss = F.cross_entropy(logits, labels)
      return loss
  
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 NSPHead obj's underlying member method code with example usage: obj.forward(inputs, labels) and returns: loss