docstr: NOTE: we neglect the impact of biases / pointwises
docstr_tok: ["NOTE",":","we","neglect","the","impact","of","biases","\/","pointwises"]
code: def operator_flop(
      cls, x: torch.Tensor, w1: torch.Tensor, b1, w2: torch.Tensor, b2
  ) -> int:
      """NOTE: we neglect the impact of biases / pointwises"""
      M, N, K = x.shape[0], w1.shape[0], w1.shape[1]
      return M * N * K * 2 * 2
  
imports: from dataclasses import dataclass
  from typing import Dict, Optional, Sequence, Tuple, Union
  import torch
  import torch.nn.functional
  from torch import nn
  from .common import BaseOperator, get_xformers_operator, register_operator
  from .unbind import stack_or_none, unbind
label_desc: Use image node_id 1 for calling the DualGemmSiluOp obj's underlying member method code with example usage: obj.operator_flop(cls, x, w1, b1, w2, b2) and returns: unknown