code: def mul(lhs, rhs):
      """
      Packed broadcasted multiplication.
      """
      if isinstance(lhs, torch.Tensor) and isinstance(
          rhs, torch.Tensor
      ):
          dims = "".join(sorted(set(lhs._pyro_dims + rhs._pyro_dims)))
          equation = lhs._pyro_dims + "," + rhs._pyro_dims + "->" + dims
          result = torch.einsum(equation, lhs, rhs, backend="torch")
          result._pyro_dims = dims
          return result
      result = lhs * rhs
      if isinstance(lhs, torch.Tensor):
          result._pyro_dims = lhs._pyro_dims
      elif isinstance(rhs, torch.Tensor):
          result._pyro_dims = rhs._pyro_dims
      return result
  
file_path: pyro/pyro/ops/packed.py
imports: import math
  import torch
  from pyro.distributions.util import is_identically_one
  from pyro.util import ignore_jit_warnings
label_desc: Use image node_id 5 for calling a global function with example usage: mul(lhs, rhs) and returns: result, result