code: def convert_from_mxnet(model, checkpoint_prefix, debug=False):
      _, mxnet_weights, mxnet_aux = mxnet.model.load_checkpoint(
          checkpoint_prefix, 0
      )
      remapped_state = {}
      for state_key in model.state_dict().keys():
          k = state_key.split(".")
          aux = False
          mxnet_key = ""
          if k[-1] == "num_batches_tracked":
              continue
          if k[0] == "features":
              if k[1] == "conv1_1":
                  # input block
                  mxnet_key += "conv1_x_1__"
                  if k[2] == "bn":
                      mxnet_key += "relu-sp__bn_"
                      aux, key_add = _convert_bn(k[3])
                      mxnet_key += key_add
                  else:
                      if k[3] != "weight":
                          raise AssertionError
                      mxnet_key += "conv_" + k[3]
              elif k[1] == "conv5_bn_ac":
                  # bn + ac at end of features block
                  mxnet_key += "conv5_x_x__relu-sp__bn_"
                  if k[2] != "bn":
                      raise AssertionError
                  aux, key_add = _convert_bn(k[3])
                  mxnet_key += key_add
              else:
                  # middle blocks
                  bc_block = bool(model.b and "c1x1_c" in k[2])
                  ck = k[1].split("_")
                  mxnet_key += ck[0] + "_x__" + ck[1] + "_"
                  ck = k[2].split("_")
                  mxnet_key += ck[0] + "-" + ck[1]
                  if ck[1] == "w" and len(ck) > 2:
                      mxnet_key += "(s/2)" if ck[2] == "s2" else "(s/1)"
                  mxnet_key += "__"
                  if k[3] == "bn":
                      mxnet_key += "bn_" if bc_block else "bn__bn_"
                      aux, key_add = _convert_bn(k[4])
                      mxnet_key += key_add
                  else:
                      ki = 3 if bc_block else 4
                      if k[ki] != "weight":
                          raise AssertionError
                      mxnet_key += "conv_" + k[ki]
          elif k[0] == "classifier":
              if "fc6-1k_weight" in mxnet_weights:
                  mxnet_key += "fc6-1k_"
              else:
                  mxnet_key += "fc6_"
              mxnet_key += k[1]
          else:
              if not False:
                  raise AssertionError("Unexpected token")
  
          if debug:
              print(mxnet_key, "=> ", state_key, end=" ")
  
          mxnet_array = (
              mxnet_aux[mxnet_key] if aux else mxnet_weights[mxnet_key]
          )
          torch_tensor = torch.from_numpy(mxnet_array.asnumpy())
          if k[0] == "classifier" and k[1] == "weight":
              torch_tensor = torch_tensor.view(
                  torch_tensor.size() + (1, 1)
              )
          remapped_state[state_key] = torch_tensor
  
          if debug:
              print(
                  list(torch_tensor.size()),
                  torch_tensor.mean(),
                  torch_tensor.std(),
              )
  
      model.load_state_dict(remapped_state)
  
      return model
  
imports: import os
  import argparse
  import torch
  from .model_factory import create_model
label_desc: Use image node_id 2 for calling a global function with example usage: convert_from_mxnet(model, checkpoint_prefix, debug) and returns: model