code: def get_classifier(input_data, num_classes, **kwargs):
      flatten = mx.sym.Flatten(data=input_data, name="flatten")
      try:
          fc6 = mx.sym.FullyConnected(
              data=flatten, num_hidden=4096, name="fc6", flatten=False
          )
          relu6 = mx.sym.Activation(
              data=fc6, act_type="relu", name="relu6"
          )
          drop6 = mx.sym.Dropout(data=relu6, p=0.5, name="drop6")
          fc7 = mx.sym.FullyConnected(
              data=drop6, num_hidden=4096, name="fc7", flatten=False
          )
          relu7 = mx.sym.Activation(
              data=fc7, act_type="relu", name="relu7"
          )
          drop7 = mx.sym.Dropout(data=relu7, p=0.5, name="drop7")
          fc8 = mx.sym.FullyConnected(
              data=drop7,
              num_hidden=num_classes,
              name="fc8",
              flatten=False,
          )
      except:
          fc6 = mx.sym.FullyConnected(
              data=flatten, num_hidden=4096, name="fc6"
          )
          relu6 = mx.sym.Activation(
              data=fc6, act_type="relu", name="relu6"
          )
          drop6 = mx.sym.Dropout(data=relu6, p=0.5, name="drop6")
          fc7 = mx.sym.FullyConnected(
              data=drop6, num_hidden=4096, name="fc7"
          )
          relu7 = mx.sym.Activation(
              data=fc7, act_type="relu", name="relu7"
          )
          drop7 = mx.sym.Dropout(data=relu7, p=0.5, name="drop7")
          fc8 = mx.sym.FullyConnected(
              data=drop7, num_hidden=num_classes, name="fc8"
          )
      return fc8
  
imports: import mxnet
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: get_classifier(input_data, num_classes) and returns: fc8