docstr: return the stats values from the factors to update and the parameters
  
  :param factors: ([TensorFlow Tensor]) The factors to update
  :param varlist: ([TensorFlow Tensor]) The parameters
  :return: ([TensorFlow Tensor]) The stats values
docstr_tok: ["return","the","stats","values","from","the","factors","to","update","and","the","parameters",":","param","factors",":","(","[","TensorFlow","Tensor","]",")","The","factors","to","update",":","param","varlist",":","(","[","TensorFlow","Tensor","]",")","The","parameters",":","return",":","(","[","TensorFlow","Tensor","]",")","The","stats","values"]
code: def get_stats(self, factors, varlist):
      """
      return the stats values from the factors to update and the parameters
  
      :param factors: ([TensorFlow Tensor]) The factors to update
      :param varlist: ([TensorFlow Tensor]) The parameters
      :return: ([TensorFlow Tensor]) The stats values
      """
      if len(self.stats) == 0:
          # initialize stats variables on CPU because eigen decomp is
          # computed on CPU
          with tf.device("/cpu"):
              tmp_stats_cache = {}
  
              # search for tensor factors and
              # use block diag approx for the bias units
              for var in varlist:
                  bprop_factor = factors[var]["bpropFactors_concat"]
                  op_type = factors[var]["opName"]
                  if op_type == "Conv2D":
                      operator_height = bprop_factor.get_shape()[1]
                      operator_width = bprop_factor.get_shape()[2]
                      if (
                          operator_height == 1
                          and operator_width == 1
                          and self._channel_fac
                      ):
                          # factorization along the channels do not support
                          # homogeneous coordinate
                          var_assn_bias = factors[var]["assnBias"]
                          if var_assn_bias:
                              factors[var]["assnBias"] = None
                              factors[var_assn_bias][
                                  "assnWeights"
                              ] = None
  
              for var in varlist:
                  fprop_factor = factors[var]["fpropFactors_concat"]
                  bprop_factor = factors[var]["bpropFactors_concat"]
                  op_type = factors[var]["opName"]
                  self.stats[var] = {
                      "opName": op_type,
                      "fprop_concat_stats": [],
                      "bprop_concat_stats": [],
                      "assnWeights": factors[var]["assnWeights"],
                      "assnBias": factors[var]["assnBias"],
                  }
                  if fprop_factor is not None:
                      if fprop_factor not in tmp_stats_cache:
                          if op_type == "Conv2D":
                              kernel_height = var.get_shape()[0]
                              kernel_width = var.get_shape()[1]
                              n_channels = fprop_factor.get_shape()[-1]
  
                              operator_height = (
                                  bprop_factor.get_shape()[1]
                              )
                              operator_width = bprop_factor.get_shape()[
                                  2
                              ]
                              if (
                                  operator_height == 1
                                  and operator_width == 1
                                  and self._channel_fac
                              ):
                                  # factorization along the channels
                                  # assume independence between input channels and spatial
                                  # 2K-1 x 2K-1 covariance matrix and C x C covariance matrix
                                  # factorization along the channels do not
                                  # support homogeneous coordinate, assnBias
                                  # is always None
                                  fprop_factor2_size = (
                                      kernel_height * kernel_width
                                  )
                                  slot_fprop_factor_stats2 = (
                                      tf.Variable(
                                          tf.diag(
                                              tf.ones(
                                                  [fprop_factor2_size]
                                              )
                                          )
                                          * self._diag_init_coeff,
                                          name="KFAC_STATS/"
                                          + fprop_factor.op.name,
                                          trainable=False,
                                      )
                                  )
                                  self.stats[var][
                                      "fprop_concat_stats"
                                  ].append(slot_fprop_factor_stats2)
  
                                  fprop_factor_size = n_channels
                              else:
                                  # 2K-1 x 2K-1 x C x C covariance matrix
                                  # assume BHWC
                                  fprop_factor_size = (
                                      kernel_height
                                      * kernel_width
                                      * n_channels
                                  )
                          else:
                              # D x D covariance matrix
                              fprop_factor_size = (
                                  fprop_factor.get_shape()[-1]
                              )
  
                          # use homogeneous coordinate
                          if (
                              not self._blockdiag_bias
                              and self.stats[var]["assnBias"]
                          ):
                              fprop_factor_size += 1
  
                          slot_fprop_factor_stats = tf.Variable(
                              tf.diag(tf.ones([fprop_factor_size]))
                              * self._diag_init_coeff,
                              name="KFAC_STATS/" + fprop_factor.op.name,
                              trainable=False,
                          )
                          self.stats[var]["fprop_concat_stats"].append(
                              slot_fprop_factor_stats
                          )
                          if op_type != "Conv2D":
                              tmp_stats_cache[
                                  fprop_factor
                              ] = self.stats[var]["fprop_concat_stats"]
                      else:
                          self.stats[var][
                              "fprop_concat_stats"
                          ] = tmp_stats_cache[fprop_factor]
  
                  if bprop_factor is not None:
                      # no need to collect backward stats for bias vectors if
                      # using homogeneous coordinates
                      if not (
                          (not self._blockdiag_bias)
                          and self.stats[var]["assnWeights"]
                      ):
                          if bprop_factor not in tmp_stats_cache:
                              slot_bprop_factor_stats = tf.Variable(
                                  tf.diag(
                                      tf.ones(
                                          [bprop_factor.get_shape()[-1]]
                                      )
                                  )
                                  * self._diag_init_coeff,
                                  name="KFAC_STATS/"
                                  + bprop_factor.op.name,
                                  trainable=False,
                              )
                              self.stats[var][
                                  "bprop_concat_stats"
                              ].append(slot_bprop_factor_stats)
                              tmp_stats_cache[
                                  bprop_factor
                              ] = self.stats[var]["bprop_concat_stats"]
                          else:
                              self.stats[var][
                                  "bprop_concat_stats"
                              ] = tmp_stats_cache[bprop_factor]
  
      return self.stats
  
imports: import re
  from functools import reduce
  import tensorflow
  import numpy
  from stable_baselines.acktr.kfac_utils import detect_min_val, factor_reshape, gmatmul
label_desc: Use image node_id 3 for calling the KfacOptimizer obj's underlying member method code with example usage: obj.get_stats(factors, varlist) and returns: self