code: def forward_gpu(self, inputs):
      self.retain_inputs((0, 1, 2))
      x, t, W = inputs
  
      self.ignore_mask = t != self.ignore_label
      samples = self._make_samples(t)
  
      n_in = x.shape[1]
      self.wx = cuda.elementwise(
          "raw T W, raw T x, bool mask, S k, int32 c, int32 m",
          "T wx",
          """
              T f = 0;
              if (mask == 1) {
                  for (int j = 0; j < c; ++j) {
                    int x_ind[] = {(i / m), j};
                    int w_ind[] = {k, j};
                    f += x[x_ind] * W[w_ind];
                  }
              }
              wx = f;
              """,
          "negative_sampling_wx",
      )(
          W,
          x,
          self.ignore_mask[:, None],
          samples,
          n_in,
          self.sample_size + 1,
      )
  
      loss = cuda.elementwise(
          "T wx, int32 c, int32 m, bool mask",
          "T y",
          """
              if (mask) {
                T f = wx;
                if (i % m == 0) {
                  f = -f;
                }
                if (f < 0) {
                  y = __logf(1 + __expf(f));
                } else {
                  y = f + __logf(1 + __expf(-f));
                }
              } else {
                y = 0;
              }
              """,
          "negative_sampling_forward",
      )(self.wx, n_in, self.sample_size + 1, self.ignore_mask[:, None])
  
      if self.reduce == "sum":
          loss = loss.sum()
      else:  # 'no':
          loss = loss.sum(axis=1)
  
      self.samples = samples
      return (loss,)
  
imports: import numpy
  import six
  import chainer
  from chainer import backend
  from chainer.backends import cuda
  from chainer import function_node
  from chainer import utils
  from chainer.utils import argument
  from chainer.utils import precision
  from chainer.utils import type_check
label_desc: Use image node_id 5 for calling the NegativeSamplingFunction obj's underlying member method code with example usage: obj.forward_gpu(inputs) and returns: loss