code: def forward_single(self, input, target, mask, beta=1.0, eps=1e-6):
      batch_size = input.shape[0]
  
      diff = paddle.abs(input - target) * mask.unsqueeze(1)
      loss = paddle.where(
          diff < beta, 0.5 * diff * diff / beta, diff - 0.5 * beta
      )
      loss = paddle.cast(loss.reshape((batch_size, -1)), "float32")
      mask = paddle.cast(mask.reshape((batch_size, -1)), "float32")
      loss = paddle.sum(loss, axis=-1)
      loss = loss / (mask.sum(axis=-1) + eps)
  
      return loss
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  from paddle import nn
  import paddle.nn.functional
  import numpy
label_desc: Use image node_id 2 for calling the SmoothL1Loss obj's underlying member method code with example usage: obj.forward_single(input, target, mask, beta, eps) and returns: loss