code: def _convert(args):
      if args.dstFramework == "caffe":
          from mmdnn.conversion.caffe.caffe_emitter import CaffeEmitter
  
          if args.IRWeightPath is None:
              emitter = CaffeEmitter(args.IRModelPath)
          else:
              assert args.dstWeightPath
              emitter = CaffeEmitter(
                  (args.IRModelPath, args.IRWeightPath)
              )
  
      elif args.dstFramework == "keras":
          from mmdnn.conversion.keras.keras2_emitter import (
              Keras2Emitter,
          )
  
          emitter = Keras2Emitter((args.IRModelPath, args.IRWeightPath))
  
      elif args.dstFramework == "tensorflow":
          from mmdnn.conversion.tensorflow.tensorflow_emitter import (
              TensorflowEmitter,
          )
  
          if args.IRWeightPath is None:
              # Convert network architecture only
              emitter = TensorflowEmitter(args.IRModelPath)
          else:
              emitter = TensorflowEmitter(
                  (args.IRModelPath, args.IRWeightPath)
              )
  
      elif args.dstFramework == "cntk":
          from mmdnn.conversion.cntk.cntk_emitter import CntkEmitter
  
          if args.IRWeightPath is None:
              emitter = CntkEmitter(args.IRModelPath)
          else:
              emitter = CntkEmitter(
                  (args.IRModelPath, args.IRWeightPath)
              )
  
      elif args.dstFramework == "coreml":
          raise NotImplementedError(
              "CoreML emitter is not finished yet."
          )
  
      elif args.dstFramework == "pytorch":
          if not args.dstWeightPath or not args.IRWeightPath:
              raise ValueError("Need to set a target weight filename.")
          from mmdnn.conversion.pytorch.pytorch_emitter import (
              PytorchEmitter,
          )
  
          emitter = PytorchEmitter(
              (args.IRModelPath, args.IRWeightPath)
          )
  
      elif args.dstFramework == "mxnet":
          from mmdnn.conversion.mxnet.mxnet_emitter import MXNetEmitter
  
          if args.IRWeightPath is None:
              emitter = MXNetEmitter(args.IRModelPath)
          else:
              if args.dstWeightPath is None:
                  raise ValueError(
                      "MXNet emitter needs argument [dstWeightPath(dw)], like -dw mxnet_converted-0000.param"
                  )
              emitter = MXNetEmitter(
                  (
                      args.IRModelPath,
                      args.IRWeightPath,
                      args.dstWeightPath,
                  )
              )
      elif args.dstFramework == "onnx":
          from mmdnn.conversion.onnx.onnx_emitter import OnnxEmitter
  
          if args.IRWeightPath is None:
              raise NotImplementedError(
                  "ONNX emitter needs IR weight file"
              )
          else:
              emitter = OnnxEmitter(args.IRModelPath, args.IRWeightPath)
      else:
          assert False
  
      emitter.run(args.dstModelPath, args.dstWeightPath, args.phase)
  
      return 0
  
imports: import sys
  import google.protobuf.text_format
  from six import text_type
label_desc: Use image node_id 1 for calling a global function with example usage: _convert(args) and returns: int