# Copyright (c) 2021. Lucas G. S. Jeub
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import torch_geometric as tg
from ..utils.mixins import EmbeddingMixin
from ..layers.decoders import DistanceDecoder
from ..layers.VGAEconv import VGAEconv
[docs]
class VGAE(tg.nn.VGAE, EmbeddingMixin):
[docs]
def __init__(self, dim, hidden_dim, num_features, dist=False):
"""
initialise a Variational Graph Auto-Encoder model
Args:
dim: output dimension
hidden_dim: inner hidden dimension
num_features: number of input features
dist: if ``True`` use distance decoder, otherwise use inner product decoder (default: ``False``)
Returns:
initialised :class:`tg.nn.VGAE` model
"""
if dist:
super().__init__(encoder=VGAEconv(dim, num_node_features=num_features, hidden_dim=hidden_dim),
decoder=DistanceDecoder())
else:
super().__init__(encoder=VGAEconv(dim, num_node_features=num_features, hidden_dim=hidden_dim))