code: def var_constraints(pattern):
      passed = True
      passed = (
          passed
          and pattern.add_or_sub.x.val is not None
          or pattern.add_or_sub.y.val is not None
      )
  
      is_sub, is_first_input = _get_is_sub_and_is_first_input(pattern)
      linear_bias, bias, Dout = _get_linear_bias_bias_Dout(
          pattern, is_first_input
      )
  
      # check if the shape is broadcasable
      passed = passed and np.prod(linear_bias.shape) == np.prod(
          bias.shape
      )
      passed = passed and bias.shape[-1] == Dout
      return passed
  
file_path: coremltools/coremltools/converters/mil/experimental/passes/generic_linear_bias_fusion.py
imports: import os
  import numpy
  from coremltools.converters.mil import Builder
  from coremltools.converters.mil.experimental.passes.generic_pass_infrastructure import register_generic_pass
  from coremltools.converters.mil.mil import get_new_symbol
label_desc: Use image node_id 1 for calling a global function with example usage: var_constraints(pattern) and returns: passed