class_docstr: This container identifies which methods need to be overridden in addition to
  the base container to enable use in the RLHF pipeline. These methods are not
  necessary for inference alone.
  
  NOTE: If you are using this feature with a container that
  also inherits from `MetaTensorContainer`, ensure that `MetaTensorContainer`
  is inherited before `HybridEngineContainer` in the class definition.
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docstr: Same purposes as the base container, but also grabs the hooks for any LoRA
  parameters. If it's necessary to override specific sub-components of the model,
  it's best to augment the specific `set_[component]` itself rather than modifying
  the `initialize_tensors` method. See the `HybridSplitQKVContainer` for an example.
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code: def initialize_tensors(self, enable_training=False):
      """
      Same purposes as the base container, but also grabs the hooks for any LoRA
      parameters. If it's necessary to override specific sub-components of the model,
      it's best to augment the specific `set_[component]` itself rather than modifying
      the `initialize_tensors` method. See the `HybridSplitQKVContainer` for an example.
      """
      super().initialize_tensors(enable_training=enable_training)
      self.set_lora_params()
  
imports: from abc import ABC, abstractmethod
  from typing import List, Tuple
  import torch
label_desc: Use image node_id 1 for calling the HybridEngineContainer obj's underlying member method code with example usage: obj.initialize_tensors(enable_training) without return types