human
Solve the following python programming problem given the following code:
def __init__(self, n_x, n_a, n_z, n_y, qa_hid, qz_hid, qy_hid, px_hid, pa_hid, nonlinearity = rectify, px_nonlinearity = None, x_dist = 'bernoulli', batchnorm = False, seed = 1234) : 
    "
        Initialize an skip deep generative model consisting of
        discriminative classifier q(y|a,x),
        generative model P p(a|z,y) and p(x|a,z,y),
        inference model Q q(a|x) and q(z|a,x,y).
        Weights are initialized using the Bengio and Glorot (2010) initialization scheme.
        :param n_x: Number of inputs.
        :param n_a: Number of auxiliary.
        :param n_z: Number of latent.
        :param n_y: Number of classes.
        :param qa_hid: List of number of deterministic hidden q(a|x).
        :param qz_hid: List of number of deterministic hidden q(z|a,x,y).
        :param qy_hid: List of number of deterministic hidden q(y|a,x).
        :param px_hid: List of number of deterministic hidden p(a|z,y) & p(x|z,y).
        :param nonlinearity: The transfer function used in the deterministic layers.
        :param x_dist: The x distribution, 'bernoulli', 'multinomial', or 'gaussian'.
        :param batchnorm: Boolean value for batch normalization.
        :param seed: The random seed.
        " 
    super (SDGMSSL, self).__init__ (n_x, (qz_hid + px_hid), (n_a + n_z), nonlinearity) 
    self.x_dist = x_dist 
    self.n_y = n_y 
    self.n_x = n_x 
    self.n_a = n_a 
    self.n_z = n_z 
    self.batchnorm = batchnorm 
    self._srng = RandomStreams (seed) 
    init_w = 0.001 
    hid_w = '' 
    if ((nonlinearity == rectify) or (nonlinearity == softplus)) : 
        hid_w = 'relu' 
self.sym_beta = T.scalar ('beta') 
    self.sym_x_l = T.matrix ('x') 
    self.sym_t_l = T.matrix ('t') 
    self.sym_x_u = T.matrix ('x') 
    self.sym_bs_l = T.iscalar ('bs_l') 
    self.sym_samples = T.iscalar ('samples') 
    self.sym_z = T.matrix ('z') 
    self.sym_a = T.matrix ('a') 
    def dense_layer(layer_in, n, dist_w = init.GlorotNormal, dist_b = init.Normal) : 
        dense = DenseLayer (layer_in, n,...

    
    

    Fix the buggy line: self.l_px_mu = (ReshapeLayer (l_px_zy_mu, ((- 1), l_qz_axy.sym_samples, 1, n_x)) if (x_dist == 'gaussian') else None)
assistant
#Here is Python program to fix the buggy line: self.

self.l_px_mu = (ReshapeLayer (l_px_zy_mu, ((- 1), self.sym_samples, 1, n_x)) if (x_dist == 'gaussian') else None)