code: def forward(self, h):
      out = self.mlp(h)
      z_pres_p = torch.sigmoid(out[:, 0 : self.z_pres_size])
      z_where_loc = out[
          :, self.z_pres_size : self.z_pres_size + self.z_where_size
      ]
      z_where_scale = softplus(
          out[:, (self.z_pres_size + self.z_where_size) :]
      )
      return z_pres_p, z_where_loc, z_where_scale
  
imports: import torch
  import torch.nn
  from torch.nn.functional import softplus
label_desc: Use image node_id 2 for calling the Predict obj's underlying member method code with example usage: obj.forward(h) and returns: z_pres_p,  z_where_loc,  z_where_scale