original_docstring: 
          Evaluates the model as a whole, encodes and decodes. Note that the log det jacobian is zero
           for a plain VAE (without flows), and z_0 = z_k.
          
docstring: Evaluates the model as a whole, encodes and decodes. Note that the log det jacobian is zero
  for a plain VAE (without flows), and z_0 = z_k.
docstring_tokens: Evaluates the model as a whole encodes and decodes . Note that the log det jacobian is zero for a plain VAE ( without flows ) and z_0 = z_k .
code: def forward(self, x):
          """
          Evaluates the model as a whole, encodes and decodes. Note that the log det jacobian is zero
           for a plain VAE (without flows), and z_0 = z_k.
          """
  
          # mean and variance of z
          z_mu, z_var = self.encode(x)
          # sample z
          z = self.reparameterize(z_mu, z_var)
          x_mean = self.decode(z)
  
          return x_mean, z_mu, z_var, self.log_det_j, z, z
code_tokens: def forward ( self , x ) : z_mu , z_var = self . encode ( x ) z = self . reparameterize ( z_mu , z_var ) x_mean = self . decode ( z ) return x_mean , z_mu , z_var , self . log_det_j , z , z
short_docstring: Evaluates the model as a whole, encodes and decodes.
short_docstring_tokens: Evaluates the model as a whole encodes and decodes .
comment: """
          Evaluates the model as a whole, encodes and decodes. Note that the log det jacobian is zero
           for a plain VAE (without flows), and z_0 = z_k.
          """$SEP$# mean and variance of z$SEP$# sample z