code: def generate_data(args):
      model = SimpleHarmonicModel(
          args.process_noise, args.measurement_noise
      )
  
      state = {}
      initial = torch.tensor([1.0, 0.0])
      model.init(state, initial=initial)
      zs = [initial]
      ys = [None]
      for t in range(args.num_timesteps):
          z, y = model.step(state)
          zs.append(z)
          ys.append(y)
  
      return zs, ys
  
file_path: pyro/examples/smcfilter.py
imports: import argparse
  import logging
  import torch
  import pyro
  import pyro.distributions
  from pyro.infer import SMCFilter
label_desc: Use image node_id 1 for calling a global function with example usage: generate_data(args) and returns: zs,  ys