code: def split_placeholders(self, placeholder, split):
      inner_placeholder_list = torch.split(
          placeholder, torch.sum(split, dim=1).tolist(), dim=0
      )
      placeholder_list = []
      for index, split_placeholder in enumerate(inner_placeholder_list):
          placeholder_list.append(
              torch.split(
                  split_placeholder, split[index].tolist(), dim=0
              )
          )
      return placeholder_list
  
imports: import logging
  from typing import Dict, List, Type, Union
  import ray
  from ray.rllib.algorithms.ppo.ppo_tf_policy import validate_config
  from ray.rllib.evaluation.postprocessing import Postprocessing, compute_gae_for_sample_batch
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchDistributionWrapper
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import ValueNetworkMixin
  from ray.rllib.policy.torch_policy_v2 import TorchPolicyV2
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.torch_utils import apply_grad_clipping
  from ray.rllib.utils.typing import TensorType
label_desc: Use image node_id 3 for calling the MAMLLoss obj's underlying member method code with example usage: obj.split_placeholders(placeholder, split) and returns: placeholder_list