code: def inner_product(self, xs, ys):
      return sum([torch.sum(x * y) for (x, y) in zip(xs, ys)])
  
file_path: deepspeed/deepspeed/runtime/eigenvalue.py
imports: import torch
  from deepspeed.utils import log_dist
  import numpy
  import logging
label_desc: Use image node_id 4 for calling the Eigenvalue obj's underlying member method code with example usage: obj.inner_product(xs, ys) and returns: sum