code: def log_prob(self, x):
      x, y = x[..., 0], x[..., 1]
      a, b = self.a, self.b
      u0 = x / a
      u1 = (y - b * (u0**2 + a**2)) * a
      return self.mvn.log_prob(torch.stack([u0, u1], dim=-1))
  
imports: import argparse
  import logging
  import os
  from functools import partial
  import matplotlib.pyplot
  import seaborn
  import torch
  from matplotlib.gridspec import GridSpec
  from torch.distributions.utils import broadcast_all
  import pyro
  import pyro.distributions
  from pyro import optim, poutine
  from pyro.distributions import constraints
  from pyro.distributions.transforms import block_autoregressive, iterated
  from pyro.infer import MCMC, NUTS, SVI, Trace_ELBO
  from pyro.infer.autoguide import AutoDiagonalNormal, AutoNormalizingFlow
  from pyro.infer.reparam import NeuTraReparam
label_desc: Use image node_id 3 for calling the BananaShaped obj's underlying member method code with example usage: obj.log_prob(x) and returns: self