| def forward( |
| self, |
| sample, |
| sample_posterior=False, |
| return_dict=True, |
| generator=None, |
| ): |
| r""" |
| Args: |
| sample (`torch.Tensor`): Input sample. |
| sample_posterior (`bool`, *optional*, defaults to `False`): |
| Whether to sample from the posterior. |
| return_dict (`bool`, *optional*, defaults to `True`): |
| Whether or not to return a [`DecoderOutput`] instead of a plain tuple. |
| """ |
| x = sample |
| posterior = self.encode(x).latent_dist |
| if sample_posterior: |
| z = posterior.sample(generator=generator) |
| else: |
| z = posterior.mode() |
| dec = self.decode(z).sample |
| return dec, None, None |
|
|
