code: def MLP(order):
      model = cnn.CNNModelHelper()
      d = 256
      depth = 20
      width = 3
      for i in range(depth):
          for j in range(width):
              current = "fc_{}_{}".format(i, j) if i > 0 else "data"
              next_ = "fc_{}_{}".format(i + 1, j)
              model.FC(
                  current,
                  next_,
                  dim_in=d,
                  dim_out=d,
                  weight_init=model.XavierInit,
                  bias_init=model.XavierInit,
              )
              model.Sum(
                  ["fc_{}_{}".format(depth, j) for j in range(width)],
                  ["sum"],
              )
              model.FC(
                  "sum",
                  "last",
                  dim_in=d,
                  dim_out=1000,
                  weight_init=model.XavierInit,
                  bias_init=model.XavierInit,
              )
              xent = model.LabelCrossEntropy(["last", "label"], "xent")
              model.AveragedLoss(xent, "loss")
              return model, d
  
file_path: pytorch/caffe2/experiments/python/convnet_benchmarks.py
imports: import argparse
  import time
  from caffe2.python import cnn, workspace, core
  import caffe2.python.SparseTransformer
label_desc: Use image node_id 1 for calling a global function with example usage: MLP(order) and returns: model,  d