code: def get_classifier(input_data, num_classes, **kwargs):
      flatten = mx.sym.Flatten(data=input_data, name="flatten")
      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