code: def quantized_matmul(context, node):
      inputs = _get_inputs(context, node, expected=4)
      assert types.is_float(inputs[0].dtype)
      assert types.is_float(inputs[1].dtype)
      x, y = promote_input_dtypes([inputs[0], inputs[1]])
      assert (
          inputs[2].val == 0 and inputs[3].val == 0
      ), "non zero scale / zero-point not supported in quantized_matmul op."
      res = mb.matmul(x=x, y=y, name=node.name)
      context.add(res)
  
imports: import numpy
  import torch
  from coremltools import _logger
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil import Var, types
  from .ops import NUM_TO_TORCH_DTYPE, _create_linear_layer, _get_inputs, promote_input_dtypes
  from .torch_op_registry import register_torch_op
label_desc: Use image node_id 10 for calling a global function with example usage: quantized_matmul(context, node) without return types