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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _verify_names(sampler, var_names, arg_names):
 """Make sure var_names and arg_names are assigned reasonably. This is meant to run before loading emcee objects into InferenceData. In case var_names or arg_names is None, will provide defaults. If they are not None, it verifies there are the right number of them. Throws a ValueError in case validation fails. Parameters sampler : emcee.EnsembleSampler Fitted emcee sampler var_names : list[str] or None Names for the emcee parameters arg_names : list[str] or None Names for the args/observations provided to emcee Returns ------- list[str], list[str] Defaults for var_names and arg_names """

    # There are 3 possible cases: emcee2, emcee3 and sampler read from h5 file (emcee3 only)
    if hasattr(sampler, "args"):
        num_vars = sampler.chain.shape[-1]
        num_args = len(sampler.args)
    elif hasattr(sampler, "log_prob_fn"):
        num_vars = sampler.get_chain().shape[-1]
        num_args = len(sampler.log_prob_fn.args)
    else:
        num_vars = sampler.get_chain().shape[-1]
        num_args = 0  # emcee only stores the posterior samples

    if var_names is None:
        var_names = ["var_{}".format(idx) for idx in range(num_vars)]
    if arg_names is None:
        arg_names = ["arg_{}".format(idx) for idx in range(num_args)]

    if len(var_names) != num_vars:
        raise ValueError(
            "The sampler has {} variables, but only {} var_names were provided!".format(
                num_vars, len(var_names)
            )
        )

    if len(arg_names) != num_args:
        raise ValueError(
            "The sampler has {} args, but only {} arg_names were provided!".format(
                num_args, len(arg_names)
            )
        )
    return var_names, arg_names