code: def forward(
      self,
      input: torch.Tensor,
      weight: torch.Tensor,
      async_op: bool = False,
  ):
      q_scale = (
          weight.scale if hasattr(weight, "scale") else torch.empty(1)
      )
      q_int8 = self.config.dtype == torch.int8
      output = self.vector_matmul_func(
          input,
          weight,
          async_op,
          q_scale,
          q_int8,
          self.config.transposed_mode,
      )
      return output
  
file_path: DeepSpeed/deepspeed/ops/transformer/inference/op_binding/vector_matmul.py
imports: import os
  import torch
  from ..config import DeepSpeedInferenceConfig
  from .base import BaseOp
  import deepspeed
label_desc: Use image node_id 3 for calling the VectorMatMulOp obj's underlying member method code with example usage: obj.forward(input, weight, async_op) and returns: output