class_docstr: Append parameters and buffers to model's positional argument list.
class_docstr_tok: ["Append","parameters","and","buffers","to","model","'s","positional","argument","list","."]
docstr: Append model's parameters and buffers into its input.
  
  Args:
      model_args: The model args.
      model_kwargs: The model kwargs.
      model: The PyTorch model.
  
  Returns:
      A tuple of the model args + appended inputs and kwargs.
code: def apply(
      self,
      model_args: Sequence[Any],
      model_kwargs: Mapping[str, Any],
      model: Optional[
          Union[torch.nn.Module, Callable, torch_export.ExportedProgram]
      ] = None,
  ) -> Tuple[Sequence[Any], Mapping[str, Any]]:
      """Append model's parameters and buffers into its input.
  
      Args:
          model_args: The model args.
          model_kwargs: The model kwargs.
          model: The PyTorch model.
  
      Returns:
          A tuple of the model args + appended inputs and kwargs.
      """
      return (*model_args, *self.inputs), model_kwargs
  
imports: from __future__ import annotations
  import inspect
  from typing import Any, Callable, List, Mapping, Optional, Protocol, runtime_checkable, Sequence, Tuple, Union
  import torch
  import torch.export
  from torch.onnx._internal import _beartype
  from torch.utils import _pytree
label_desc: Use image node_id 2 for calling the LiftParametersAndBuffersIntoArgsInputStep obj's underlying member method code with example usage: obj.apply(model_args, model_kwargs, model) and returns: unknown,  unknown,  model_kwargs