src/transformers/models/pegasus/modeling_flax_pegasus.py/FlaxPegasusEncoderLayerCollection/__call__
class FlaxPegasusEncoderLayerCollection:    def __call__(
        self,
        hidden_states,
        attention_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        all_attentions = () if output_attentions else None
        all_hidden_states = () if output_hidden_states else None

        for encoder_layer in self.layers:
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)
            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
            dropout_probability = random.uniform(0, 1)
            if not deterministic and (dropout_probability < self.layerdrop):  # skip the layer
                layer_outputs = (None, None)
            else:
                layer_outputs = encoder_layer(
                    hidden_states,
                    attention_mask,
                    output_attentions,
                    deterministic,
                )
            hidden_states = layer_outputs[0]
            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[1],)

        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        outputs = (hidden_states, all_hidden_states, all_attentions)

        if not return_dict:
            return tuple(v for v in outputs if v is not None)

        return FlaxBaseModelOutput(
            last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
        )