code: def forward(
      ctx, A, B, out=None, bias=None, quant_state: F.QuantState = None
  ):
      # default of pytorch behavior if inputs are empty
      ctx.is_empty = False
      if prod(A.shape) == 0:
          ctx.is_empty = True
          ctx.A = A
          ctx.B = B
          ctx.bias = bias
          B_shape = quant_state.shape
          if A.shape[-1] == B_shape[0]:
              return torch.empty(
                  A.shape[:-1] + B_shape[1:],
                  dtype=A.dtype,
                  device=A.device,
              )
          else:
              return torch.empty(
                  A.shape[:-1] + B_shape[:1],
                  dtype=A.dtype,
                  device=A.device,
              )
  
      # 1. Dequantize
      # 2. MatmulnN
      output = torch.nn.functional.linear(
          A, F.dequantize_4bit(B, quant_state).to(A.dtype).t(), bias
      )
  
      # 3. Save state
      ctx.state = quant_state
      ctx.dtype_A, ctx.dtype_B, ctx.dtype_bias = (
          A.dtype,
          B.dtype,
          None if bias is None else bias.dtype,
      )
  
      if any(ctx.needs_input_grad[:2]):
          ctx.tensors = (A, B)
      else:
          ctx.tensors = (None, None)
  
      return output
  
imports: import operator
  import warnings
  from dataclasses import dataclass
  from functools import reduce
  from typing import Tuple, Optional, List
  from warnings import warn
  import torch
  import bitsandbytes.functional
label_desc: Use image node_id 1 for calling the MatMul4Bit obj's underlying member method code with example usage: obj.forward(ctx, A, B, out, bias, quant_state) and returns: output, torch, torch