def forward(self, p, inputs, sample=True, **kwargs):
    ' Performs a forward pass through the network.\n        Args:\n            p (tensor): sampled points\n            inputs (tensor): conditioning input\n            sample (bool): whether to sample for z\n        '
    batch_size = p.size(0)
    c = self.encode_inputs(inputs)
    z = self.get_z_from_prior((batch_size,), sample=sample)
    p_r = self.decode(p, z, c, **kwargs)
    return p_r
Performs a forward pass through the network.
Args:
    p (tensor): sampled points
    inputs (tensor): conditioning input
    sample (bool): whether to sample for z
def forward(self, p, inputs, sample=True, **kwargs):
    ' Performs a forward pass through the network.\n        Args:\n            p (tensor): sampled points\n            inputs (tensor): conditioning input\n            sample (bool): whether to sample for z\n        '
    batch_size = p.size(0)
    c = self.encode_inputs(inputs)
    z = self.get_z_from_prior((batch_size,), sample=sample)
    p_r = self.decode(p, z, c, **kwargs)
    return p_r