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.
code: def __init__(self, in_size, counts, sample_size):
      super(BlackOut, self).__init__()
      vocab_size = len(counts)
      p = numpy.array(counts, dtype=numpy.float32)
      self.sampler = walker_alias.WalkerAlias(p)
      self.sample_size = sample_size
  
      with self.init_scope():
          self.W = variable.Parameter(shape=(vocab_size, in_size))
  
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 1 to create a new BlackOut object from inherited base classes: link with example: obj = BlackOut(in_size, counts, sample_size)