code: def _generate_lstm_config(self):
      # assign some variables to reduce verbosity
      name = self.name
      # in the below code we will just call descriptor_strings as descriptors for conciseness
      input_dim = self.descriptors["input"]["dim"]
      input_descriptor = self.descriptors["input"]["final-string"]
      cell_dim = self.config["cell-dim"]
      bottleneck_dim = self.config["bottleneck-dim"]
      self_scale = self.config["self-scale"]
      delay = self.config["delay"]
      affine_str = self.config["ng-affine-options"]
      l2_regularize = self.config["l2-regularize"]
      l2_regularize_option = (
          "l2-regularize={0} ".format(l2_regularize)
          if l2_regularize != 0.0
          else ""
      )
      decay_time = self.config["decay-time"]
      # we expect decay_time to be either -1, or large, like 10 or 50.
      recurrence_scale = (
          1.0 if decay_time < 0 else 1.0 - (abs(delay) / decay_time)
      )
      assert (
          recurrence_scale > 0
      )  # or user may have set decay-time much
      # too small.
      bptrunc_str = (
          "clipping-threshold={0}"
          " zeroing-threshold={1}"
          " zeroing-interval={2}"
          " recurrence-interval={3}"
          " scale={4}"
          "".format(
              self.config["clipping-threshold"],
              self.config["zeroing-threshold"],
              self.config["zeroing-interval"],
              abs(delay),
              recurrence_scale,
          )
      )
      lstm_str = self.config["lstm-nonlinearity-options"]
  
      configs = []
  
      # See XconfigFastLstmLayer to understand what's going on here.  This
      # differs from that code by a factorization of the W_all matrix into two
      # pieces with a smaller dimension in between (with the first of the two
      # pieces constrained to have orthonormal rows).  Note: we don't apply l2
      # regularization to this layer, since, with the orthonormality
      # constraint, it's meaningless.
      configs.append("### Begin LSTM layer '{0}'".format(name))
      configs.append(
          "component name={0}.W_all_a type=LinearComponent input-dim={1} "
          "orthonormal-constraint={2} output-dim={3} {4}".format(
              name,
              input_dim + cell_dim,
              self.config["orthonormal-constraint"],
              bottleneck_dim,
              affine_str,
          )
      )
  
      configs.append(
          "component name={0}.W_all_b type=LinearComponent input-dim={1} "
          "output-dim={2} {3} {4}".format(
              name,
              bottleneck_dim,
              cell_dim * 4,
              affine_str,
              l2_regularize_option,
          )
      )
      configs.append(
          "component name={0}.W_all_b_so type=ScaleAndOffsetComponent dim={1} "
          "max-change=0.75".format(name, cell_dim * 4)
      )
  
      configs.append(
          "# The core LSTM nonlinearity, implemented as a single component."
      )
      configs.append(
          "# Input = (i_part, f_part, c_part, o_part, c_{t-1}), output = (c_t, m_t)"
      )
      configs.append(
          "# See cu-math.h:ComputeLstmNonlinearity() for details."
      )
      configs.append(
          "component name={0}.lstm_nonlin type=LstmNonlinearityComponent "
          "cell-dim={1} {2} {3}".format(
              name, cell_dim, lstm_str, l2_regularize_option
          )
      )
      configs.append(
          "# Component for backprop truncation, to avoid gradient blowup in long training examples."
      )
  
      configs.append(
          "component name={0}.cm_trunc type=BackpropTruncationComponent dim={1} {2}".format(
              name, 2 * cell_dim, bptrunc_str
          )
      )
      configs.append(
          "component name={0}.m_batchnorm type=BatchNormComponent dim={1} ".format(
              name, cell_dim
          )
      )
  
      configs.append("###  Nodes for the components above.")
      configs.append(
          "component-node name={0}.W_all_a component={0}.W_all_a input=Append({1}, "
          "IfDefined(Offset(Scale({2}, {0}.m_trunc), {3})))".format(
              name, input_descriptor, self_scale, delay
          )
      )
      configs.append(
          "component-node name={0}.W_all_b component={0}.W_all_b "
          "input={0}.W_all_a".format(name)
      )
      configs.append(
          "component-node name={0}.W_all_b_so component={0}.W_all_b_so "
          "input={0}.W_all_b".format(name)
      )
  
      configs.append(
          "component-node name={0}.lstm_nonlin component={0}.lstm_nonlin "
          "input=Append({0}.W_all_b_so, IfDefined(Offset({0}.c_trunc, {1})))".format(
              name, delay
          )
      )
      configs.append(
          "dim-range-node name={0}.m input-node={0}.lstm_nonlin dim-offset={1} "
          "dim={1}".format(name, cell_dim)
      )
      configs.append(
          "component-node name={0}.cm_trunc component={0}.cm_trunc input={0}.lstm_nonlin".format(
              name
          )
      )
      configs.append(
          "dim-range-node name={0}.c_trunc input-node={0}.cm_trunc dim-offset=0 "
          "dim={1}".format(name, cell_dim)
      )
      configs.append(
          "dim-range-node name={0}.m_trunc input-node={0}.cm_trunc dim-offset={1} "
          "dim={1}".format(name, cell_dim)
      )
      configs.append(
          "component-node name={0}.m_batchnorm component={0}.m_batchnorm "
          "input={0}.m".format(name)
      )
      configs.append("### End LSTM layer '{0}'".format(name))
      return configs
  
imports: from __future__ import print_function
  import math
  import re
  import sys
  from libs.nnet3.xconfig.basic_layers import XconfigLayerBase
label_desc: Use image node_id 9 for calling the XconfigLstmbLayer obj's underlying member method code with example usage: obj._generate_lstm_config() and returns: configs