code: def sample_tree(
      edge_logits, init_edges=None, mcmc_steps=1, backend="python"
  ):
      edges = init_edges
      if edges is None:
          edges = sample_tree_approx(edge_logits, backend=backend)
      for step in range(mcmc_steps):
          edges = sample_tree_mcmc(edge_logits, edges, backend=backend)
      return edges
  
imports: import itertools
  import math
  import torch
  from torch.distributions import constraints
  from torch.distributions.utils import lazy_property
  from pyro.distributions.torch_distribution import TorchDistribution
label_desc: Use image node_id 11 for calling a global function with example usage: sample_tree(edge_logits, init_edges, mcmc_steps, backend) and returns: edges