code: def forward(self, z, inference=False):
      n_batch_axes = 1 if inference else 2
      h = F.tanh(self.linear(z, n_batch_axes=n_batch_axes))
      h = self.output(h, n_batch_axes=n_batch_axes)
      return D.Independent(
          D.Bernoulli(logit=h, binary_check=self.binary_check),
          reinterpreted_batch_ndims=1,
      )
  
imports: import numpy
  import chainer
  import chainer.distributions
  import chainer.functions
  import chainer.links
  from chainer import reporter
label_desc: Use image node_id 2 for calling the Decoder obj's underlying member method code with example usage: obj.forward(z, inference) and returns: D