code: def _generate_cnn_config(self):
      configs = []
  
      name = self.name
  
      # These 3 variables will be updated as we add components.
      cur_num_filters = self.config["num-filters-in"]
      cur_height = self.config["height-in"]
      cur_descriptor = self.descriptors["input"]["final-string"]
  
      # note: the [:-1] is to remove the '-layer'.
      operations = self.layer_type.split("-")[:-1]
      if operations[-1] == "noconv":
          operations = operations[:-1]
      # e.g.:
      # operations = [ 'conv', 'relu', 'batchnorm' ]
      # or:
      # operations = [ 'relu', 'conv', 'renorm' ]
  
      for operation in operations:
          if operation == "conv":
              a = []
              for opt_name in [
                  "param-stddev",
                  "bias-stddev",
                  "use-natural-gradient",
                  "max-change",
                  "rank-in",
                  "rank-out",
                  "num-minibatches-history",
                  "alpha-in",
                  "alpha-out",
                  "num-filters-in",
                  "num-filters-out",
                  "height-in",
                  "height-out",
                  "height-subsample-out",
                  "height-offsets",
                  "time-offsets",
                  "required-time-offsets",
                  "learning-rate-factor",
                  "l2-regularize",
              ]:
                  value = self.config[opt_name]
                  if value != "":
                      a.append("{0}={1}".format(opt_name, value))
              conv_opts = " ".join(a)
  
              configs.append(
                  "component name={0}.conv type=TimeHeightConvolutionComponent "
                  "{1}".format(name, conv_opts)
              )
              configs.append(
                  "component-node name={0}.conv component={0}.conv "
                  "input={1}".format(name, cur_descriptor)
              )
              cur_num_filters = self.config["num-filters-out"]
              cur_height = self.config["height-out"]
          elif operation == "batchnorm":
              configs.append(
                  "component name={0}.batchnorm  type=BatchNormComponent dim={1} "
                  "block-dim={2} target-rms={3}".format(
                      name,
                      cur_num_filters * cur_height,
                      cur_num_filters,
                      self.config["target-rms"],
                  )
              )
              configs.append(
                  "component-node name={0}.batchnorm component={0}.batchnorm "
                  "input={1}".format(name, cur_descriptor)
              )
          elif operation == "renorm":
              configs.append(
                  "component name={0}.renorm type=NormalizeComponent "
                  "dim={1} target-rms={2}".format(
                      name,
                      cur_num_filters * cur_height,
                      self.config["target-rms"],
                  )
              )
              configs.append(
                  "component-node name={0}.renorm component={0}.renorm "
                  "input={1}".format(name, cur_descriptor)
              )
          elif operation == "relu":
              configs.append(
                  "component name={0}.relu type=RectifiedLinearComponent "
                  "dim={1} block-dim={2} self-repair-scale={3} "
                  "self-repair-lower-threshold={4}".format(
                      name,
                      cur_num_filters * cur_height,
                      cur_num_filters,
                      self.config["self-repair-scale"],
                      self.config["self-repair-lower-threshold"],
                  )
              )
              configs.append(
                  "component-node name={0}.relu component={0}.relu "
                  "input={1}".format(name, cur_descriptor)
              )
          elif operation == "dropout":
              configs.append(
                  "component name={0}.dropout type=DropoutComponent "
                  "dim={1} dropout-proportion={2}".format(
                      name,
                      cur_num_filters * cur_height,
                      self.config["dropout-proportion"],
                  )
              )
              configs.append(
                  "component-node name={0}.dropout component={0}.dropout "
                  "input={1}".format(name, cur_descriptor)
              )
          elif operation == "so":
              configs.append(
                  "component name={0}.so type=ScaleAndOffsetComponent "
                  "dim={1} block-dim={2}".format(
                      name,
                      cur_num_filters * cur_height,
                      cur_num_filters,
                  )
              )
              configs.append(
                  "component-node name={0}.so component={0}.so "
                  "input={1}".format(name, cur_descriptor)
              )
          else:
              raise RuntimeError(
                  "Un-handled operation type: " + operation
              )
  
          cur_descriptor = "{0}.{1}".format(name, operation)
  
      return configs
  
imports: from __future__ import print_function
  from __future__ import division
  import math
  import re
  import sys
  from libs.nnet3.xconfig.basic_layers import XconfigLayerBase
label_desc: Use image node_id 10 for calling the XconfigConvLayer obj's underlying member method code with example usage: obj._generate_cnn_config() and returns: configs