code: def __init__(
      self,
      def_path,
      data_path,
      target_toolkit,
      input_shape=None,
      phase="test",
  ):
      self.layer_name_map = {}
      self.data_injector = None
      self.is_train_proto = False
      self.input_shape = input_shape
      if def_path is None:
          if self.input_shape is None:
              raise ConversionError(
                  "if the graph prototxt is not provided, the input shape should be provided"
              )
          self.input_shape = [1] + self.input_shape
          (
              def_path,
              self.data_injector,
          ) = self.gen_prototxt_from_caffemodel(
              data_path, self.input_shape
          )
          self.is_train_proto = True
      else:
          model = get_caffe_resolver().NetParameter()
          with open(def_path, "r") as f:
              text_format.Merge(f.read(), model)
          layers = model.layers or model.layer
          if (
              len(
                  [
                      layer
                      for layer in layers
                      if NodeKind.map_raw_kind(layer.type)
                      in LAYER_IN_TRAIN_PROTO
                  ]
              )
              > 0
          ):
              if self.input_shape is None:
                  raise ConversionError(
                      "the train_val.prototxt should be provided with the input shape"
                  )
              self.input_shape = [1] + self.input_shape
              self.is_train_proto = True
      graph = GraphBuilder(
          def_path, self.input_shape, self.is_train_proto, phase
      ).build()
      if self.is_train_proto:
          def_path = graph.prototxt
      if data_path is not None:
          graph = graph.transformed(
              [
                  self.data_injector
                  if self.data_injector
                  else DataInjector(
                      def_path, data_path
                  ),  # Load and associate learned parameters
                  BatchNormScaleBiasFuser(),
                  BatchNormPreprocessor(),  # Pre-process batch normalization data
              ]
          )
          target_toolkit = target_toolkit.lower()
          if target_toolkit not in ("caffe", "caffe2"):
              graph = graph.transformed(
                  [
                      DataReshaper(
                          {  # Reshape the parameters to TensorFlow's ordering
                              NodeKind.Convolution: (
                                  2,
                                  3,
                                  1,
                                  0,
                              ),  # (c_o, c_i, h, w) -> (h, w, c_i, c_o)
                              NodeKind.Deconvolution: (
                                  2,
                                  3,
                                  1,
                                  0,
                              ),  # (c_o, c_i, h, w) -> (h, w, c_i, c_o)
                              NodeKind.InnerProduct: (
                                  1,
                                  0,
                              ),  # (c_o, c_i) -> (c_i, c_o)
                          }
                      ),
                      ParameterNamer(),  # Convert parameters to dictionaries
                  ]
              )
      self.graph = graph
      #  self.graph = NodeRenamer()(graph)
      print(self.graph)
  
imports: from __future__ import unicode_literals
  from google.protobuf import text_format
  import numpy
  from mmdnn.conversion.caffe.graph import GraphBuilder, NodeKind, LAYER_IN_TRAIN_PROTO
  from mmdnn.conversion.caffe.mapper import NodeMapper, get_handler_name
  from mmdnn.conversion.caffe.resolver import get_caffe_resolver, has_pycaffe
  from mmdnn.conversion.caffe.errors import print_stderr, ConversionError
  from mmdnn.conversion.caffe.common_graph import Graph
  from mmdnn.conversion.caffe.utils import get_lower_case, get_upper_case
label_desc: Use image node_id 1 to create a new CaffeTransformer object from inherited base classes: object with example: obj = CaffeTransformer(def_path, data_path, target_toolkit, input_shape, phase)