class_docstr: BlackOut loss layer.
  
  .. seealso:: :func:`~chainer.functions.black_out` for more detail.
  
  Args:
      in_size (int): Dimension of input vectors.
      counts (int list): Number of each identifiers.
      sample_size (int): Number of negative samples.
  
  Attributes:
      W (~chainer.Parameter): Weight parameter matrix.
docstr: Computes the loss value for given input and ground truth labels.
  
  Args:
      x (~chainer.Variable): Input of the weight matrix multiplication.
      t (~chainer.Variable): Batch of ground truth labels.
  
  Returns:
      ~chainer.Variable: Loss value.
code: def forward(self, x, t):
      """Computes the loss value for given input and ground truth labels.
  
      Args:
          x (~chainer.Variable): Input of the weight matrix multiplication.
          t (~chainer.Variable): Batch of ground truth labels.
  
      Returns:
          ~chainer.Variable: Loss value.
  
      """
  
      batch_size = x.shape[0]
      if self.sample_data is not None:
          # for test
          sample_data = self.sample_data
      else:
          shape = (batch_size, self.sample_size)
          sample_data = self.sampler.sample(shape)
      samples = variable.Variable(sample_data, requires_grad=False)
      return black_out.black_out(x, t, self.W, samples)
  
file_path: chainer/chainer/links/loss/black_out.py
imports: import numpy
  from chainer.functions.loss import black_out
  from chainer import link
  from chainer.utils import walker_alias
  from chainer import variable
label_desc: Use image node_id 3 for calling the BlackOut obj's underlying member method code with example usage: obj.forward(x, t) and returns: black_out