code: def __init__(self, a, b, rho=0.9):
      self.a, self.b, self.rho = broadcast_all(a, b, rho)
      self.mvn = dist.MultivariateNormal(
          torch.tensor([0.0, 0.0]),
          covariance_matrix=torch.tensor(
              [[1.0, self.rho], [self.rho, 1.0]]
          ),
      )
      super().__init__(event_shape=(2,))
  
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 1 to create a new BananaShaped object from inherited base classes: dist with example: obj = BananaShaped(a, b, rho)