def make_minibatch_iterator(self, data: DataProto) -> Iterable[DataProto]:
        """Make minibatch iterator for updating the actor

        Args:
            data (DataProto): a DataProto containing keys

                ``input_ids``: tensor of shape [batch_size, sequence_length]. torch.int64, where ``sequence_length = prompt_length + response_length``

                ``attention_mask``: tensor of shape [batch_size, sequence_length]. torch.int64

                ``position_ids``: tensor of shape [batch_size, sequence_length]. torch.int64

                ``responses``: tensor of shape [batch_size, response_length]. torch.int64. Note that responses = input_ids[:, -response_length:]

                ``old_log_probs``: tensor of shape [batch_size, response_length]. torch.float32. The log probability of responses.

                ``advantages``: tensor of shape [batch_size, response_length]. torch.float32. The advantages of responses.
                See PPO paper for details. https://arxiv.org/abs/1707.06347

        Returns:

        """
        select_keys = ['responses', 'input_ids', 'attention_mask', 'position_ids', 'old_log_probs', 'advantages']
        data = data.select(batch_keys=select_keys)
        return data.make_iterator(mini_batch_size=self.config.ppo_mini_batch_size,
                                  epochs=self.config.ppo_epochs,
                                  dataloader_kwargs={'shuffle': self.config.shuffle})
Make minibatch iterator for updating the actor

        Args:
            data (DataProto): a DataProto containing keys

                ``input_ids``: tensor of shape [batch_size, sequence_length]. torch.int64, where ``sequence_length = prompt_length + response_length``

                ``attention_mask``: tensor of shape [batch_size, sequence_length]. torch.int64

                ``position_ids``: tensor of shape [batch_size, sequence_length]. torch.int64

                ``responses``: tensor of shape [batch_size, response_length]. torch.int64. Note that responses = input_ids[:, -response_length:]

                ``old_log_probs``: tensor of shape [batch_size, response_length]. torch.float32. The log probability of responses.

                ``advantages``: tensor of shape [batch_size, response_length]. torch.float32. The advantages of responses.
                See PPO paper for details. https://arxiv.org/abs/1707.06347

        Returns:

        
verl/workers/actor/megatron_actor.py