class_docstr: This "checkpoint" engine uses the existing interface to enable loading parameters into an
  inference model from a model already instantiated in memory. In general, this is not the
  recommended way to use the inference engine, and should only be used when absolutely necessary.
  
  The primary limitation of this approach is that the model must be fully instantiated in memory.
  In a tensor parallel scenario, this means that the model is either replicated many times in host
  memory. Currently, it is also recommended to only use this approach for models held in host memory.
  
  In order to free the memory held by this copy of the model, we delete the model in the first call
  to `parameters`, so it is not safe to make this call twice.
docstr: Create virtual checkpoint engine for the provided module.
  
  Args:
      model (torch.nn.Module): Model to load parameters from.
docstr_tok: ["Create","virtual","checkpoint","engine","for","the","provided","module",".","Args",":","model","(","torch.nn.Module",")",":","Model","to","load","parameters","from","."]
code: def __init__(self, model: torch.nn.Module) -> None:
      """
      Create virtual checkpoint engine for the provided module.
  
      Args:
          model (torch.nn.Module): Model to load parameters from.
      """
      super().__init__()
      self.model = model
  
imports: from typing import Iterable, Tuple
  import torch
  from .base_engine import CheckpointEngineBase
label_desc: Use image node_id 1 to create a new InMemoryModelEngine object from inherited base classes: CheckpointEngineBase with example: obj = InMemoryModelEngine(model)