code: def MLP(order, cudnn_ws):
      model = model_helper.ModelHelper(name="MLP")
      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)
              brew.fc(
                  model,
                  current,
                  next_,
                  dim_in=d,
                  dim_out=d,
                  weight_init=("XavierFill", {}),
                  bias_init=("XavierFill", {}),
              )
      brew.sum(
          model,
          ["fc_{}_{}".format(depth, j) for j in range(width)],
          ["sum"],
      )
      brew.fc(
          model,
          "sum",
          "last",
          dim_in=d,
          dim_out=1000,
          weight_init=("XavierFill", {}),
          bias_init=("XavierFill", {}),
      )
      xent = model.net.LabelCrossEntropy(["last", "label"], "xent")
      model.net.AveragedLoss(xent, "loss")
      return model, d
  
file_path: pytorch/caffe2/python/convnet_benchmarks.py
imports: import argparse
  from caffe2.python import workspace, brew, model_helper
label_desc: Use image node_id 1 for calling a global function with example usage: MLP(order, cudnn_ws) and returns: model,  d