code: def bmm(self, batch2):
      if config.coordinate_descent_tuning:
          if self.shape[1] == 1 or batch2.shape[2] == 1:
              out = (self.unsqueeze(-1) * batch2.unsqueeze(1)).sum(
                  dim=2
              )
              return out
      if self.device.type == "cpu":
          if self.size(1) == 1 and batch2.size(-1) == 1:
              return torch.sum(
                  self.squeeze(1) * batch2.squeeze(-1),
                  dim=1,
                  keepdim=True,
              ).unsqueeze(1)
      return NotImplemented
  
imports: import functools
  import logging
  import math
  import sys
  import typing
  from typing import Optional
  import torch
  import torch._decomp
  import torch._prims_common
  import torch.ao.quantization.fx._decomposed
  from torch._decomp import core_aten_decompositions, get_decompositions, remove_decompositions
  from torch._decomp.decompositions import _grid_sampler_2d, pw_cast_for_opmath
  from torch._decomp.decompositions_for_rng import extra_random_decomps
  from torch._higher_order_ops.out_dtype import out_dtype
  from torch._prims_common import elementwise_dtypes, ELEMENTWISE_TYPE_PROMOTION_KIND, type_to_dtype
  from .None import config, inductor_prims
label_desc: Use image node_id 11 for calling a global function with example usage: bmm(batch2) and returns: NotImplemented, out, torch