human
Solve the following python programming problem given the following code:
@change_flags(compute_test_value='ignore')
def advi_minibatch(vars=None, start=None, model=None, n=5000, n_mcsamples=1, minibatch_RVs=None, minibatch_tensors=None, minibatches=None, global_RVs=None, local_RVs=None, observed_RVs=None, encoder_params=None, total_size=None, optimizer=None, learning_rate=0.001, epsilon=0.1, random_seed=None, mode=None):
assistant
#Here is the Python code you requested.

    """Perform mini-batch ADVI.
    This function implements a mini-batch automatic differentiation variational
    inference (ADVI; Kucukelbir et al., 2015) with the meanfield
    approximation. Autoencoding variational Bayes (AEVB; Kingma and Welling,
    2014) is also supported.
    For explanation, we classify random variables in probabilistic models into
    three types. Observed random variables
    :math:`{\cal Y}=\{\mathbf{y}_{i}\}_{i=1}^{N}` are :math:`N` observations.
    Each :math:`\mathbf{y}_{i}` can be a set of observed random variables,
    i.e., :math:`\mathbf{y}_{i}=\{\mathbf{y}_{i}^{k}\}_{k=1}^{V_{o}}`, where
    :math:`V_{k}` is the number of the types of observed random variables
    in the model.
    The next ones are global random variables
    :math:`\Theta=\{\theta^{k}\}_{k=1}^{V_{g}}`, which are used to calculate
    the probabilities for all observed samples.
    The last ones are local random variables
    :math:`{\cal Z}=\{\mathbf{z}_{i}\}_{i=1}^{N}`, where
    :math:`\mathbf{z}_{i}=\{\mathbf{z}_{i}^{k}\}_{k=1}^{V_{l}}`.
    These RVs are used only in AEVB.
    The goal of ADVI is to approximate the posterior distribution
    :math:`p(\Theta,{\cal Z}|{\cal Y})` by variational posterior
    :math:`q(\Theta)\prod_{i=1}^{N}q(\mathbf{z}_{i})`. All of these terms
    are normal distributions (mean-field approximation).
    :math:`q(\Theta)` is parametrized with its means and standard deviations.
    These parameters are denoted as :math:`\gamma`. While :math:`\gamma` is
    a constant, the parameters of :math:`q(\mathbf{z}_{i})` are dependent on
    each observation. Therefore these parameters are denoted as
    :math:`\xi(\mathbf{y}_{i}; \nu)`, where :math:`\nu` is the parameters
    of :math:`\xi(\cdot)`. For example, :math:`\xi(\cdot)` can be a
    multilayer perceptron or convolutional neural network.
    In addition to :math:`\xi(\cdot)`, we can also include deterministic
    mappings for the likelihood of observations. We denote the parameters of
    the deterministic mappings as..."""
    import warnings
    warnings.warn('Old    ADVI    interface    is    deprecated    and    be    removed    in    future,    use    pm.ADVI    instead', DeprecationWarning, stacklevel=2)
    if (encoder_params is None):
        encoder_params = []
    model = pm.modelcontext(model)
    vars = inputvars((vars if (vars is not None) else model.vars))
    start = (start if (start is not None) else model.test_point)
    if (not pm.model.all_continuous(vars)):
        raise ValueError('Model    can    not    include    discrete    RVs    for    ADVI.')
    _check_minibatches(minibatch_tensors, minibatches)
    if (encoder_params is None):
        encoder_params = []
    if (optimizer is None):
        optimizer = adagrad_optimizer(learning_rate, epsilon)
    (local_RVs, observed_RVs) = _get_rvss(minibatch_RVs, local_RVs, observed_RVs, minibatch_tensors, total_size)
    def get_transformed(v):
        if hasattr(v, 'transformed'):
            return v.transformed
        return v
    local_RVs = OrderedDict([(get_transformed(v), (uw, s)) for (v, (uw, s)) in local_RVs.items()])
    grvs = list((set(vars) - set((list(local_RVs) + list(observed_RVs)))))
    if (global_RVs is None):
        global_RVs = OrderedDict({v: 1 for v in grvs})
    elif (len(grvs) != len(global_RVs)):
        _value_error('global_RVs    ({})    must    have    all    global    RVs:    {}'.format([v for v in global_RVs], grvs))
    (elbo, uw_l, uw_g) = _make_elbo_t(observed_RVs, global_RVs, local_RVs, model.potentials, n_mcsamples, random_seed)
    replaces = dict()
    if (0 < len(global_RVs)):
        (uw_global_shared, bij) = _init_uw_global_shared(start, global_RVs)
        replaces.update({uw_g: uw_global_shared})
    if (0 < len(local_RVs)):
        uws = [uw for (_, (uw, _)) in local_RVs.items()]
        uw_local_encoded = tt.concatenate(([uw[0].ravel() for uw in uws] + [uw[1].ravel() for uw in uws]))
        replaces.update({uw_l: uw_local_encoded})
    elbo = theano.clone(elbo, OrderedDict(replaces), strict=False)
    def...