code: def benchmark_float(elemwise_add, broadcast=False):
      header = (
          operator_string(elemwise_add)
          + ", float"
          + (" , broadcast" if broadcast else "")
      )
      print_header(header)
      for shape, nhid in sizes:
          net = FCWithSum(shape[1], nhid, elemwise_add)
          net.initialize()
          net.hybridize(static_alloc=True, static_shape=True)
          data0 = mx.np.random.uniform(size=shape, low=-1.0, high=1.0)
          data1 = mx.np.random.uniform(size=shape, low=-1.0, high=1.0)
          shape2 = (shape[0], nhid)
          if broadcast and not elemwise_add:
              # broadcast is allowed only for npi_add version
              shape2 = (1, 1)
          data2 = mx.np.random.uniform(size=shape2, low=-1.0, high=1.0)
          net.optimize_for(data0, data1, data2, backend="ONEDNN")
          measure(net, data0, data1, data2, shape, nhid)
      dump_graph_fn(net, operator_string(elemwise_add) + "_float")
  
imports: import time
  import gc
  import sys
  import mxnet
  from mxnet.gluon import nn
  from mxnet.contrib import quantization
label_desc: Use image node_id 6 for calling a global function with example usage: benchmark_float(elemwise_add, broadcast) without return types