class_docstr: Variational AutoEncoder
code: def __init__(self, n_in, n_latent, n_h):
      super(VAE, self).__init__()
      with self.init_scope():
          # encoder
          self.le1 = L.Linear(n_in, n_h)
          self.le2_mu = L.Linear(n_h, n_latent)
          self.le2_ln_var = L.Linear(n_h, n_latent)
          # decoder
          self.ld1 = L.Linear(n_latent, n_h)
          self.ld2 = L.Linear(n_h, n_in)
  
file_path: tensorboardX/examples/chainer/plain_logger/net.py
imports: import six
  import chainer
  import chainer.functions
  from chainer.functions.loss.vae import gaussian_kl_divergence
  import chainer.links
label_desc: Use image node_id 1 to create a new VAE object from inherited base classes: chainer with example: obj = VAE(n_in, n_latent, n_h)