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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 rhat(data, var_names=None):
 r"""Compute estimate of Split R-hat for a set of traces. The Split R-hat diagnostic tests for lack of convergence by comparing the variance between multiple chains to the variance within each chain. If convergence has been achieved, the between-chain and within-chain variances should be identical. To be most effective in detecting evidence for nonconvergence, each chain should have been initialized to starting values that are dispersed relative to the target distribution. Parameters data : obj Any object that can be converted to an az.InferenceData object Refer to documentation of az.convert_to_dataset for details At least 2 posterior chains are needed to compute this diagnostic of one or more stochastic parameters. For ndarray: shape = (chain, draw). var_names : list Names of variables to include in the rhat report Returns ------- r_hat : xarray.Dataset Returns dictionary of the potential scale reduction factors, :math:`\hat{R}` Notes ----- The diagnostic is computed by: .. math:: \hat{R} = \frac{\hat{V}}{W} where :math:`W` is the within-chain variance and :math:`\hat{V}` is the posterior variance estimate for the pooled traces. This is the potential scale reduction factor, which converges to unity when each of the traces is a sample from the target posterior. Values greater than one indicate that one or more chains have not yet converged. References Gelman et al. BDA (2014) Brooks and Gelman (1998) Gelman and Rubin (1992) """

    if isinstance(data, np.ndarray):
        return _get_split_rhat(data)

    dataset = convert_to_dataset(data, group="posterior")
    var_names = _var_names(var_names, dataset)

    dataset = dataset if var_names is None else dataset[var_names]
    return xr.apply_ufunc(_rhat_ufunc, dataset, input_core_dims=(("chain", "draw"),))