code: def sample(self, sample_shape=()):
      u = self.mvn.sample(sample_shape)
      u0, u1 = u[..., 0], u[..., 1]
      a, b = self.a, self.b
      x = a * u0
      y = (u1 / a) + b * (u0**2 + a**2)
      return torch.stack([x, y], -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 2 for calling the BananaShaped obj's underlying member method code with example usage: obj.sample(sample_shape) and returns: torch