def decode(self, latent_tensor, observation_shape, is_training, reuse=False): """Decodes the latent_tensor to an observation.""" return architectures.make_decoder(latent_tensor, observation_shape, is_training=is_training, reuse=reuse)
def forward_pass(self, latent_tensor, observation_shape, is_training): """Decodes the latent_tensor to an observation.""" return architectures.make_decoder(latent_tensor, observation_shape, is_training=is_training)
def decode(self, latent_tensor, observation_shape, is_training): """Decodes the latent_tensor to an observation without features.""" return get_return_v( architectures.make_decoder(latent_tensor, observation_shape, is_training=is_training), 1)
def decode_with_gfeats(self, latent_tensor, observation_shape, is_training): """Decodes the latent_tensor to an observation.""" return architectures.make_decoder(latent_tensor, observation_shape, is_training=is_training)