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"]
      rec_proj_dim = self.config["recurrent-projection-dim"]
      nonrec_proj_dim = self.config["non-recurrent-projection-dim"]
      delay = self.config["delay"]
      repair_nonlin = self.config["self-repair-scale-nonlinearity"]
      repair_nonlin_str = (
          "self-repair-scale={0:.10f}".format(repair_nonlin)
          if repair_nonlin is not None
          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,
          )
      )
      affine_str = self.config["ng-affine-options"]
      pes_str = self.config["ng-per-element-scale-options"]
      dropout_proportion = self.config["dropout-proportion"]
      dropout_per_frame = (
          "true" if self.config["dropout-per-frame"] else "false"
      )
  
      # Natural gradient per element scale parameters
      if (
          re.search("param-mean", pes_str) is None
          and re.search("param-stddev", pes_str) is None
      ):
          pes_str += " param-mean=0.0 param-stddev=1.0 "
      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("# Input gate control : W_i* matrices")
      configs.append(
          "component name={0}.W_i.xr type=NaturalGradientAffineComponent input-dim={1} "
          "output-dim={2} {3} {4}".format(
              name,
              input_dim + rec_proj_dim,
              cell_dim,
              affine_str,
              l2_regularize_option,
          )
      )
      configs.append(
          "# note : the cell outputs pass through a diagonal matrix"
      )
      configs.append(
          "component name={0}.w_i.c type=NaturalGradientPerElementScaleComponent "
          "dim={1} {2} {3}".format(
              name, cell_dim, pes_str, l2_regularize_option
          )
      )
      configs.append("# Forget gate control : W_f* matrices")
      configs.append(
          "component name={0}.W_f.xr type=NaturalGradientAffineComponent input-dim={1} "
          "output-dim={2} {3} {4}".format(
              name,
              input_dim + rec_proj_dim,
              cell_dim,
              affine_str,
              l2_regularize_option,
          )
      )
      configs.append(
          "# note : the cell outputs pass through a diagonal matrix"
      )
      configs.append(
          "component name={0}.w_f.c type=NaturalGradientPerElementScaleComponent  "
          "dim={1} {2} {3}".format(
              name, cell_dim, pes_str, l2_regularize_option
          )
      )
  
      configs.append("#  Output gate control : W_o* matrices")
      configs.append(
          "component name={0}.W_o.xr type=NaturalGradientAffineComponent input-dim={1} "
          "output-dim={2} {3} {4}".format(
              name,
              input_dim + rec_proj_dim,
              cell_dim,
              affine_str,
              l2_regularize_option,
          )
      )
      configs.append(
          "# note : the cell outputs pass through a diagonal matrix"
      )
      configs.append(
          "component name={0}.w_o.c type=NaturalGradientPerElementScaleComponent "
          "dim={1} {2} {3}".format(
              name, cell_dim, pes_str, l2_regularize_option
          )
      )
  
      configs.append("# Cell input matrices : W_c* matrices")
      configs.append(
          "component name={0}.W_c.xr type=NaturalGradientAffineComponent input-dim={1} "
          "output-dim={2} {3} {4}".format(
              name,
              input_dim + rec_proj_dim,
              cell_dim,
              affine_str,
              l2_regularize_option,
          )
      )
  
      configs.append("# Defining the non-linearities")
      configs.append(
          "component name={0}.i type=SigmoidComponent dim={1} {2}".format(
              name, cell_dim, repair_nonlin_str
          )
      )
      configs.append(
          "component name={0}.f type=SigmoidComponent dim={1} {2}".format(
              name, cell_dim, repair_nonlin_str
          )
      )
      configs.append(
          "component name={0}.o type=SigmoidComponent dim={1} {2}".format(
              name, cell_dim, repair_nonlin_str
          )
      )
      configs.append(
          "component name={0}.g type=TanhComponent dim={1} {2}".format(
              name, cell_dim, repair_nonlin_str
          )
      )
      configs.append(
          "component name={0}.h type=TanhComponent dim={1} {2}".format(
              name, cell_dim, repair_nonlin_str
          )
      )
      if dropout_proportion != -1.0:
          configs.append(
              "component name={0}.dropout type=DropoutComponent dim={1} "
              "dropout-proportion={2} dropout-per-frame={3}".format(
                  name, cell_dim, dropout_proportion, dropout_per_frame
              )
          )
      configs.append(
          "# Defining the components for other cell computations"
      )
      configs.append(
          "component name={0}.c1 type=ElementwiseProductComponent input-dim={1} output-dim={2}"
          "".format(name, 2 * cell_dim, cell_dim)
      )
      configs.append(
          "component name={0}.c2 type=ElementwiseProductComponent input-dim={1} output-dim={2}"
          "".format(name, 2 * cell_dim, cell_dim)
      )
      configs.append(
          "component name={0}.m type=ElementwiseProductComponent input-dim={1} output-dim={2}"
          "".format(name, 2 * cell_dim, cell_dim)
      )
      configs.append(
          "component name={0}.c type=BackpropTruncationComponent dim={1} {2}"
          "".format(name, cell_dim, bptrunc_str)
      )
  
      # c1_t and c2_t defined below
      configs.append(
          "component-node name={0}.c_t component={0}.c input=Sum({0}.c1_t, {0}.c2_t)".format(
              name
          )
      )
      delayed_c_t_descriptor = "IfDefined(Offset({0}.c_t, {1}))".format(
          name, delay
      )
  
      recurrent_connection = "{0}.r_t".format(name)
      configs.append("# i_t")
      configs.append(
          "component-node name={0}.i1_t component={0}.W_i.xr input=Append({1}, IfDefined(Offset({2}, {3})))"
          "".format(name, input_descriptor, recurrent_connection, delay)
      )
      configs.append(
          "component-node name={0}.i2_t component={0}.w_i.c  input={1}".format(
              name, delayed_c_t_descriptor
          )
      )
      if dropout_proportion != -1.0:
          configs.append(
              "component-node name={0}.i_t_predrop component={0}.i input=Sum({0}.i1_t, {0}.i2_t)".format(
                  name
              )
          )
          configs.append(
              "component-node name={0}.i_t component={0}.dropout input={0}.i_t_predrop".format(
                  name
              )
          )
      else:
          configs.append(
              "component-node name={0}.i_t component={0}.i input=Sum({0}.i1_t, {0}.i2_t)".format(
                  name
              )
          )
  
      configs.append("# f_t")
      configs.append(
          "component-node name={0}.f1_t component={0}.W_f.xr input=Append({1}, IfDefined(Offset({2}, {3})))"
          "".format(name, input_descriptor, recurrent_connection, delay)
      )
      configs.append(
          "component-node name={0}.f2_t component={0}.w_f.c  input={1}".format(
              name, delayed_c_t_descriptor
          )
      )
      if dropout_proportion != -1.0:
          configs.append(
              "component-node name={0}.f_t_predrop component={0}.f input=Sum({0}.f1_t, {0}.f2_t)".format(
                  name
              )
          )
          configs.append(
              "component-node name={0}.f_t component={0}.dropout input={0}.f_t_predrop".format(
                  name
              )
          )
      else:
          configs.append(
              "component-node name={0}.f_t component={0}.f input=Sum({0}.f1_t, {0}.f2_t)".format(
                  name
              )
          )
  
      configs.append("# o_t")
      configs.append(
          "component-node name={0}.o1_t component={0}.W_o.xr input=Append({1}, IfDefined(Offset({2}, {3})))".format(
              name, input_descriptor, recurrent_connection, delay
          )
      )
      configs.append(
          "component-node name={0}.o2_t component={0}.w_o.c input={0}.c_t".format(
              name
          )
      )
      if dropout_proportion != -1.0:
          configs.append(
              "component-node name={0}.o_t_predrop component={0}.o input=Sum({0}.o1_t, {0}.o2_t)".format(
                  name
              )
          )
          configs.append(
              "component-node name={0}.o_t component={0}.dropout input={0}.o_t_predrop".format(
                  name
              )
          )
      else:
          configs.append(
              "component-node name={0}.o_t component={0}.o input=Sum({0}.o1_t, {0}.o2_t)".format(
                  name
              )
          )
  
      configs.append("# h_t")
      configs.append(
          "component-node name={0}.h_t component={0}.h input={0}.c_t".format(
              name
          )
      )
  
      configs.append("# g_t")
      configs.append(
          "component-node name={0}.g1_t component={0}.W_c.xr input=Append({1}, IfDefined(Offset({2}, {3})))"
          "".format(name, input_descriptor, recurrent_connection, delay)
      )
      configs.append(
          "component-node name={0}.g_t component={0}.g input={0}.g1_t".format(
              name
          )
      )
  
      configs.append("# parts of c_t")
      configs.append(
          "component-node name={0}.c1_t component={0}.c1  input=Append({0}.f_t, {1})".format(
              name, delayed_c_t_descriptor
          )
      )
      configs.append(
          "component-node name={0}.c2_t component={0}.c2 input=Append({0}.i_t, {0}.g_t)".format(
              name
          )
      )
  
      configs.append("# m_t")
      configs.append(
          "component-node name={0}.m_t component={0}.m input=Append({0}.o_t, {0}.h_t)".format(
              name
          )
      )
  
      # add the recurrent connections
      configs.append("# projection matrices : Wrm and Wpm")
      configs.append(
          "component name={0}.W_rp.m 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(
          "component name={0}.r type=BackpropTruncationComponent dim={1} {2}"
          "".format(name, rec_proj_dim, bptrunc_str)
      )
  
      configs.append(
          "# r_t and p_t : rp_t will be the output (if we're not doing batchnorm)"
      )
      configs.append(
          "component-node name={0}.rp_t component={0}.W_rp.m input={0}.m_t"
          "".format(name)
      )
      configs.append(
          "dim-range-node name={0}.r_t_preclip input-node={0}.rp_t dim-offset=0 "
          "dim={1}".format(name, rec_proj_dim)
      )
      configs.append(
          "component-node name={0}.r_t component={0}.r input={0}.r_t_preclip".format(
              name
          )
      )
  
      if self.layer_type == "lstmp-batchnorm-layer":
          # Add the batchnorm component, if requested to include batchnorm.
          configs.append(
              "component name={0}.rp_t_batchnorm type=BatchNormComponent dim={1} ".format(
                  name, rec_proj_dim + nonrec_proj_dim
              )
          )
          configs.append(
              "component-node name={0}.rp_t_batchnorm component={0}.rp_t_batchnorm "
              "input={0}.rp_t".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 XconfigLstmpLayer obj's underlying member method code with example usage: obj._generate_lstm_config() and returns: configs