class_docstr: Abstract interface for checkpoint engines to implement.
  
  There is no ``__init__`` method here by design, since the creation of the checkpoint
  engine will happen outside the policy/engine code. The tradeoff being made here is
  that we will write different frontends for different checkpoint engines, but these
  frontends can be tailored to the specific checkpoint engine/model source needs.
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docstr: This method should create a generator of tuples of the form (name, parameter) for
  all parameters in the model. The name should be the fully qualified name of the
  parameter, and the parameter should be a torch.Tensor.
  
  The expected use of a checkpoint engine is the following:
  ```python
  for name, parameter in checkpoint_engine.parameters():
      container_map.map_param(name, parameter)
  ```
  For a concrete use example, see ``InferenceV2Policy``.
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code: def parameters(self) -> Iterable[Tuple[str, torch.Tensor]]:
      """
      This method should create a generator of tuples of the form (name, parameter) for
      all parameters in the model. The name should be the fully qualified name of the
      parameter, and the parameter should be a torch.Tensor.
  
      The expected use of a checkpoint engine is the following:
      ```python
      for name, parameter in checkpoint_engine.parameters():
          container_map.map_param(name, parameter)
      ```
      For a concrete use example, see ``InferenceV2Policy``.
      """
      ...
  
imports: from abc import ABC, abstractmethod
  from typing import Iterable, Tuple
  import torch
label_desc: Use image node_id 1 for calling the CheckpointEngineBase obj's underlying member method code with example usage: obj.parameters() without return types