class_docstr: CMA-ES stochastic optimizer class with ask-and-tell interface, using JAX backend.
  Args:
      param_size:
          A parameter size.
      pop_size:
          A population size (optional).
          If not specified, a proper value will be inferred from param_size.
      mean:
          Initial mean vector of multi-variate gaussian distributions (optional).
          If specified, it must have a dimension of (param_size, ).
          If not specified, a default value of all-zero array will be used.
      init_stdev:
          Initial standard deviation of covariance matrix (optional).
          If not specified, a default value of 0.1 will be used.
      seed:
          A seed number (optional).
      cov:
          A covariance matrix (optional).
      logger:
          A logging.Logger instance (optional).
          If not specified, a new one will be created.
      enable_numeric_check:
          A bool indicating whether to enable numeric check (optional).
          If True, a numeric check for mean and standard deviation is enabled.
          The default value is False.
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code: def best_params(self) -> jnp.ndarray:
      return jnp.array(self.state.mean, copy=True)
  
imports: from typing import NamedTuple
  from functools import partial
  import logging
  import math
  from typing import Optional
  import numpy
  import jax
  from jax import numpy
  from evojax.algo.base import NEAlgorithm
  from evojax.util import create_logger
label_desc: Use image node_id 12 for calling the CMA_ES_JAX obj's underlying member method code with example usage: obj.best_params() and returns: jnp