def infer_z(self, p, occ, c, **kwargs):
    ' Infers z.\n        Args:\n            p (tensor): points tensor\n            occ (tensor): occupancy values for occ\n            c (tensor): latent conditioned code c\n        '
    batch_size = p.size(0)
    mean_z = torch.empty(batch_size, 0).to(self.device)
    logstd_z = torch.empty(batch_size, 0).to(self.device)
    q_z = dist.Normal(mean_z, torch.exp(logstd_z))
    return q_z
Infers z.
Args:
    p (tensor): points tensor
    occ (tensor): occupancy values for occ
    c (tensor): latent conditioned code c
def infer_z(self, p, occ, c, **kwargs):
    ' Infers z.\n        Args:\n            p (tensor): points tensor\n            occ (tensor): occupancy values for occ\n            c (tensor): latent conditioned code c\n        '
    batch_size = p.size(0)
    mean_z = torch.empty(batch_size, 0).to(self.device)
    logstd_z = torch.empty(batch_size, 0).to(self.device)
    q_z = dist.Normal(mean_z, torch.exp(logstd_z))
    return q_z