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"]
      delay = self.config["delay"]
      rec_proj_dim = self.config["recurrent-projection-dim"]
      nonrec_proj_dim = self.config["non-recurrent-projection-dim"]
      affine_str = self.config["ng-affine-options"]
      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"]
      dropout_proportion = self.config["dropout-proportion"]
      l2_regularize = self.config["l2-regularize"]
      l2_regularize_option = (
          "l2-regularize={0} ".format(l2_regularize)
          if l2_regularize != 0.0
          else ""
      )
  
      configs = []
  
      # the equations implemented here are from Sak et. al. "Long Short-Term
      # Memory Recurrent Neural Network Architectures for Large Scale Acoustic
      # Modeling"
      # https://arxiv.org/pdf/1402.1128.pdf
      # See equations (7) to (14).
      # naming convention
      # <layer-name>.W_<outputname>.<input_name> e.g. Lstm1.W_i.xr for matrix providing output to gate i and operating on an appended vector [x,r]
      configs.append("##  Begin LSTM layer '{0}'".format(name))
      configs.append(
          "# Gate control: contains W_i, W_f, W_c and W_o matrices as blocks."
      )
      configs.append(
          "component name={0}.W_all type=NaturalGradientAffineComponent input-dim={1} "
          "output-dim={2} {3} {4}".format(
              name,
              input_dim + rec_proj_dim,
              cell_dim * 4,
              affine_str,
              l2_regularize_option,
          )
      )
      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} "
          "use-dropout={2} {3} {4}".format(
              name,
              cell_dim,
              "true" if dropout_proportion != -1.0 else "false",
              lstm_str,
              l2_regularize_option,
          )
      )
      configs.append(
          "# Component for backprop truncation, to avoid gradient blowup in long training examples."
      )
      configs.append(
          "component name={0}.cr_trunc type=BackpropTruncationComponent "
          "dim={1} {2}".format(
              name, cell_dim + rec_proj_dim, bptrunc_str
          )
      )
      if dropout_proportion != -1.0:
          configs.append(
              "component name={0}.dropout_mask type=DropoutMaskComponent output-dim=3 "
              "dropout-proportion={1} ".format(name, dropout_proportion)
          )
      configs.append(
          "# Component specific to 'projected' LSTM (LSTMP), contains both recurrent"
      )
      configs.append("# and non-recurrent projections")
      configs.append(
          "component name={0}.W_rp type=NaturalGradientAffineComponent "
          "input-dim={1} output-dim={2} {3} {4}".format(
              name,
              cell_dim,
              rec_proj_dim + nonrec_proj_dim,
              affine_str,
              l2_regularize_option,
          )
      )
      configs.append("###  Nodes for the components above.")
      configs.append(
          "component-node name={0}.W_all component={0}.W_all input=Append({1}, "
          "IfDefined(Offset({0}.r_trunc, {2})))".format(
              name, input_descriptor, delay
          )
      )
  
      if dropout_proportion != -1.0:
          # note: the 'input' is a don't-care as the component never uses it; it's required
          # in component-node lines.
          configs.append(
              "component-node name={0}.dropout_mask component={0}.dropout_mask "
              "input={0}.dropout_mask".format(name)
          )
          configs.append(
              "component-node name={0}.lstm_nonlin component={0}.lstm_nonlin "
              "input=Append({0}.W_all, IfDefined(Offset({0}.c_trunc, {1})), "
              "{0}.dropout_mask)".format(name, delay)
          )
      else:
          configs.append(
              "component-node name={0}.lstm_nonlin component={0}.lstm_nonlin "
              "input=Append({0}.W_all, IfDefined(Offset({0}.c_trunc, {1})))".format(
                  name, delay
              )
          )
      configs.append(
          "dim-range-node name={0}.c input-node={0}.lstm_nonlin "
          "dim-offset=0 dim={1}".format(name, cell_dim)
      )
      configs.append(
          "dim-range-node name={0}.m input-node={0}.lstm_nonlin "
          "dim-offset={1} dim={1}".format(name, cell_dim)
      )
      configs.append(
          "# {0}.rp is the output node of this layer (if we're not "
          "including batchnorm)".format(name)
      )
      configs.append(
          "component-node name={0}.rp component={0}.W_rp input={0}.m".format(
              name
          )
      )
      configs.append(
          "dim-range-node name={0}.r input-node={0}.rp dim-offset=0 "
          "dim={1}".format(name, rec_proj_dim)
      )
      configs.append(
          "# Note: it's not 100% efficient that we have to stitch the c"
      )
      configs.append(
          "# and r back together to truncate them but it probably"
      )
      configs.append("# makes the deriv truncation more accurate .")
      configs.append(
          "component-node name={0}.cr_trunc component={0}.cr_trunc "
          "input=Append({0}.c, {0}.r)".format(name)
      )
      configs.append(
          "dim-range-node name={0}.c_trunc input-node={0}.cr_trunc "
          "dim-offset=0 dim={1}".format(name, cell_dim)
      )
      configs.append(
          "dim-range-node name={0}.r_trunc input-node={0}.cr_trunc "
          "dim-offset={1} dim={2}".format(name, cell_dim, rec_proj_dim)
      )
      if self.layer_type == "fast-lstmp-batchnorm-layer":
          # Add the batchnorm component, if requested to include batchnorm.
          configs.append(
              "component name={0}.rp_batchnorm type=BatchNormComponent dim={1} ".format(
                  name, rec_proj_dim + nonrec_proj_dim
              )
          )
          configs.append(
              "component-node name={0}.rp_batchnorm component={0}.rp_batchnorm "
              "input={0}.rp".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 XconfigFastLstmpLayer obj's underlying member method code with example usage: obj._generate_lstm_config() and returns: configs