def _decode(channels: int) -> Callable[[jnp.ndarray], jnp.ndarray]:
return hk.Sequential([
hk.GroupNorm(8),
_upsample,
_conv(channels),
nn.leaky_relu,
hk.GroupNorm(8),
_upsample,
_conv(channels),
nn.leaky_relu,
hk.GroupNorm(8),
_upsample,
_conv(channels),
nn.leaky_relu,
])
shape=(BATCH_SIZE, 2, 2, 3)),
ModuleDescriptor(
name="Bias",
create=lambda: hk.Bias(),
shape=(BATCH_SIZE, 3, 3, 3)),
ModuleDescriptor(
name="Flatten",
create=lambda: hk.Flatten(),
shape=(BATCH_SIZE, 3, 3, 3)),
ModuleDescriptor(
name="InstanceNorm",
create=lambda: hk.InstanceNorm(True, True),
shape=(BATCH_SIZE, 3, 2)),
ModuleDescriptor(
name="GroupNorm",
create=lambda: hk.GroupNorm(5),
shape=(BATCH_SIZE, 4, 4, 10)),
ModuleDescriptor(
name="LayerNorm",
create=lambda: hk.LayerNorm(1, True, True),
shape=(BATCH_SIZE, 3, 2)),
ModuleDescriptor(
name="MultiHeadAttention",
create=lambda: MultiInput( # pylint: disable=g-long-lambda
hk.MultiHeadAttention(num_heads=8, key_size=64, w_init_scale=1.0),
num_inputs=3),
shape=(BATCH_SIZE, 3, 2)),
ModuleDescriptor(
name="RMSNorm",
create=lambda: hk.RMSNorm(1),
shape=(BATCH_SIZE, 3, 2)),
