code: def linear_cx(cx, w_in, w_out, *, bias=False, num_locations=1):
      """Accumulates complexity of linear into cx = (h, w, flops, params, acts)."""
      h, w, flops, params, acts = (
          cx["h"],
          cx["w"],
          cx["flops"],
          cx["params"],
          cx["acts"],
      )
      flops += w_in * w_out * num_locations + (
          w_out * num_locations if bias else 0
      )
      params += w_in * w_out + (w_out if bias else 0)
      acts += w_out * num_locations
      return {
          "h": h,
          "w": w,
          "flops": flops,
          "params": params,
          "acts": acts,
      }
  
imports: import numpy
  import torch
  import torch.nn
  from pycls.core.config import cfg
  from torch.nn import Module
label_desc: Use image node_id 15 for calling a global function with example usage: linear_cx(cx, w_in, w_out) and returns: dict