code: def forward(self, covariates, data=None):
      coeff = pyro.sample("coeff", dist.Normal(0, 1))
      bias = pyro.sample("bias", dist.Normal(0, 1))
      scale = pyro.sample("scale", dist.LogNormal(0, 1))
  
      # Since we'll use a distributed dataloader during training, we need to
      # manually pass minibatches of (covariates,data) that are smaller than
      # the full self.size. In particular we cannot rely on pyro.plate to
      # automatically subsample, since that would lead to all workers drawing
      # identical subsamples.
      with pyro.plate("data", self.size, len(covariates)):
          loc = bias + coeff * covariates
          return pyro.sample("obs", dist.Normal(loc, scale), obs=data)
  
file_path: pyro/examples/svi_horovod.py
imports: import argparse
  import torch
  import torch.multiprocessing
  import pyro
  import pyro.distributions
  from pyro.infer import SVI, Trace_ELBO
  from pyro.infer.autoguide import AutoNormal
  from pyro.nn import PyroModule
  from pyro.optim import Adam, HorovodOptimizer
label_desc: Use image node_id 2 for calling the Model obj's underlying member method code with example usage: obj.forward(covariates, data) and returns: pyro