src/transformers/models/bart/modeling_flax_bart.py/FlaxBartDecoderPreTrainedModel/init_weights
class FlaxBartDecoderPreTrainedModel:    def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
        # init input tensors
        input_ids = jnp.zeros(input_shape, dtype="i4")
        attention_mask = jnp.ones_like(input_ids)

        batch_size, sequence_length = input_ids.shape
        position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))

        params_rng, dropout_rng = jax.random.split(rng)
        rngs = {"params": params_rng, "dropout": dropout_rng}
        encoder_hidden_states = jnp.zeros(input_shape + (self.config.d_model,))
        encoder_attention_mask = attention_mask
        module_init_outputs = self.module.init(
            rngs,
            input_ids,
            attention_mask,
            position_ids,
            encoder_hidden_states,
            encoder_attention_mask,
            return_dict=False,
        )
        return module_init_outputs["params"]