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add pyrefly suppressions for jax.jit annotation change #348

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copybara-service merged 1 commit into from
Aug 6, 2026
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add pyrefly suppressions for jax.jit annotation change #348

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8 changes: 4 additions & 4 deletions learned_optimization/learned_optimizers/nn_adam.py
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Original file line number Diff line number Diff line change
Expand Up @@ -231,13 +231,13 @@ def init(self, key: PRNGKey) -> lopt_base.MetaParams:
self.rnn_to_controls.init(key3,
jnp.zeros([0, self.lstm_hidden_size])),
"per_layer_lr":
_scaled_lr.forward(self.initial_learning_rate),
_scaled_lr.forward(self.initial_learning_rate), # pyrefly: ignore[bad-argument-type]
"per_layer_beta1":
_scaled_one_minus_log.forward(self.initial_beta1),
_scaled_one_minus_log.forward(self.initial_beta1), # pyrefly: ignore[bad-argument-type]
"per_layer_beta2":
_scaled_one_minus_log.forward(self.initial_beta2),
_scaled_one_minus_log.forward(self.initial_beta2), # pyrefly: ignore[bad-argument-type]
"per_layer_epsilon":
_scaled_epsilon.forward(self.initial_epsilon),
_scaled_epsilon.forward(self.initial_epsilon), # pyrefly: ignore[bad-argument-type]
})

def opt_fn(self,
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2 changes: 1 addition & 1 deletion learned_optimization/tasks/es_wrapper.py
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Expand Up @@ -52,7 +52,7 @@ def _fn(key: PRNGKey) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]:
pos = _sample_perturbations(theta, key, std=std)
p_theta = jax.tree_util.tree_map(lambda t, a: t + a, theta, pos)
n_theta = jax.tree_util.tree_map(lambda t, a: t - a, theta, pos)
return pos, p_theta, n_theta
return pos, p_theta, n_theta # pyrefly: ignore[bad-return]

keys = jax.random.split(key, num_samples)
vec_pos, vec_p_theta, vec_n_theta = jax.vmap(_fn)(keys)
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