code: def tell(self, evals_no_sort: np.ndarray) -> None:
      sorted_indices = sort_indices_by(evals_no_sort, self.z)
      best_eval_id = sorted_indices[0]
      f_best = evals_no_sort[best_eval_id]
      x_best = self.x[:, best_eval_id]
      self.z = self.z[:, sorted_indices]
      y = self.y[:, sorted_indices]
      x = self.x[:, sorted_indices]
      self.no_of_evals += self.lamb
      self.g += 1
      if f_best < self.f_best:
          self.f_best = f_best
          self.x_best = x_best
  
      # This operation assumes that if the solution is infeasible, infinity comes in as input.
      lambF = jnp.sum(evals_no_sort < jnp.finfo(float).max)
      # evolution path p_sigma
      self.ps = (1 - self.cs) * self.ps + jnp.sqrt(
          self.cs * (2.0 - self.cs) * self.mueff
      ) * jnp.dot(self.z, self.w_rank)
      ps_norm = jnp.linalg.norm(self.ps)
      # distance weight
      f1 = (
          self.h_inv
          * min(1.0, math.sqrt(self.lamb / self.dim))
          * math.sqrt(lambF / self.lamb)
      )
      w_tmp = self.w_rank_hat * jnp.exp(
          jnp.linalg.norm(self.z, axis=0) * f1
      ).reshape((self.lamb, 1))
      weights_dist = w_tmp / sum(w_tmp) - 1.0 / self.lamb
      # switching weights and learning rate
      weights = weights_dist if ps_norm >= self.chiN else self.w_rank
      eta_sigma = (
          self.eta_move_sigma
          if ps_norm >= self.chiN
          else self.eta_stag_sigma(lambF)
          if ps_norm >= 0.1 * self.chiN
          else self.eta_conv_sigma(lambF)
      )
      # update pc, m
      wxm = jnp.dot(x - self.m, weights)
      self.pc = (1.0 - self.cc) * self.pc + jnp.sqrt(
          self.cc * (2.0 - self.cc) * self.mueff
      ) * wxm / self.sigma
      self.m += self.eta_m * wxm
      normv4 = self.normv2**2
      exY = jnp.append(y, self.pc / self.D, axis=1)  # dim x lamb+1
      yy = exY * exY  # dim x lamb+1
      ip_yvbar = jnp.dot(self.vbar.T, exY)
      yvbar = exY * self.vbar  # dim x lamb+1. exYのそれぞれの列にvbarがかかる
      gammav = 1.0 + self.normv2
      vbarbar = self.vbar * self.vbar
      alphavd = min(
          [
              1,
              math.sqrt(
                  normv4
                  + (2 * gammav - math.sqrt(gammav)) / jnp.max(vbarbar)
              )
              / (2 + self.normv2),
          ]
      )  # scalar
      t = (
          exY * ip_yvbar - self.vbar * (ip_yvbar**2 + gammav) / 2
      )  # dim x lamb+1
      b = -(1 - alphavd**2) * normv4 / gammav + 2 * alphavd**2
      H = (
          jnp.ones([self.dim, 1]) * 2 - (b + 2 * alphavd**2) * vbarbar
      )  # dim x 1
      invH = H ** (-1)
      s_step1 = (
          yy
          - self.normv2 / gammav * (yvbar * ip_yvbar)
          - jnp.ones([self.dim, self.lamb + 1])
      )  # dim x lamb+1
      ip_vbart = jnp.dot(self.vbar.T, t)  # 1 x lamb+1
      s_step2 = s_step1 - alphavd / gammav * (
          (2 + self.normv2) * (t * self.vbar)
          - self.normv2 * jnp.dot(vbarbar, ip_vbart)
      )  # dim x lamb+1
      invHvbarbar = invH * vbarbar
      ip_s_step2invHvbarbar = jnp.dot(
          invHvbarbar.T, s_step2
      )  # 1 x lamb+1
      div = 1 + b * jnp.dot(vbarbar.T, invHvbarbar)
      if jnp.amin(abs(div)) == 0:
          self.logger.info("error: div is zero")
          return
      s = (s_step2 * invH) - b / div * jnp.dot(
          invHvbarbar, ip_s_step2invHvbarbar
      )  # dim x lamb+1
      ip_svbarbar = jnp.dot(vbarbar.T, s)  # 1 x lamb+1
      t = t - alphavd * (
          (2 + self.normv2) * (s * self.vbar)
          - jnp.dot(self.vbar, ip_svbarbar)
      )  # dim x lamb+1
      # update v, D
      exw = jnp.append(
          self.eta_B(lambF) * weights,
          jnp.full((1, 1), self.c1(lambF)),
          axis=0,
      )  # lamb+1 x 1
      self.v = self.v + jnp.dot(t, exw) / self.normv
      self.D = self.D + jnp.dot(s, exw) * self.D
      # calculate detA
      if jnp.amin(self.D) < 0:
          self.logger.info("error: invalid D")
          return
      nthrootdetA = exp(
          jnp.sum(jnp.log(self.D)) / self.dim
          + jnp.log(1 + jnp.dot(self.v.T, self.v)[0][0])
          / (2 * self.dim)
      )
      self.D = self.D / nthrootdetA
      # update sigma
      G_s = (
          jnp.sum(
              jnp.dot(
                  (self.z * self.z - jnp.ones([self.dim, self.lamb])),
                  weights,
              )
          )
          / self.dim
      )
      self.sigma = self.sigma * exp(eta_sigma / 2 * G_s)
  
imports: import math
  import numpy
  from typing import Union
  from typing import Optional
  import logging
  import jax
  import jax.numpy
  from evojax.algo.base import NEAlgorithm
  from evojax.util import create_logger
label_desc: Use image node_id 4 for calling the CRFM obj's underlying member method code with example usage: obj.tell(evals_no_sort) without return types