code: def prepare_zip_archive(cls, file, device="CPU", **kwargs):
      with zipfile.ZipFile(file, mode="r") as z:
          with z.open("__MODEL_PROTO", "r") as f:
              model = onnx.load(f)
          blob_names = set(z.namelist()) - set("__MODEL_PROTO")
          # TODO: make this more efficient
          raw_values_dict = {}
          for name in blob_names:
              with z.open(name, "r") as blob_file:
                  raw_values_dict[name] = blob_file.read()
  
      return cls.prepare(
          model, device, raw_values_dict=raw_values_dict, **kwargs
      )
  
imports: import collections
  import sys
  import zipfile
  import itertools
  import onnx.backend
  from caffe2.python import core, workspace, rnn_cell, gru_cell
  from caffe2.python.model_helper import ModelHelper
  from caffe2.proto import caffe2_pb2
  import caffe2.python.utils
  import numpy
  import onnx
  from onnx import TensorProto
  import onnx.numpy_helper
  import onnx.defs
  import onnx.shape_inference
  import onnx.utils
  from onnx.backend.base import Backend, Device, DeviceType, namedtupledict
  from caffe2.python.onnx.workspace import Workspace
  from caffe2.python.onnx.backend_rep import Caffe2Rep
  import caffe2.python._import_c_extension
  import warnings
label_desc: Use image node_id 17 for calling the Caffe2Backend obj's underlying member method code with example usage: obj.prepare_zip_archive(cls, file, device) and returns: cls