code: def transform_pattern(pattern):
      # remove all the ops, and replace with a prelu op + transpose op
      perm = pattern.transpose.perm.val
      out_var = pattern.out_op.outputs[0]
      if pattern.alpha_mul.x.val is not None:
          alpha = pattern.alpha_mul.x.val
      else:
          alpha = pattern.alpha_mul.y.val
  
      alpha_vector = -1 * alpha.flatten()
      x = mb.prelu(
          x=pattern.root_var,
          alpha=alpha_vector,
          before_op=pattern.out_op,
      )
      x = mb.transpose(
          x=x, perm=perm, name=out_var.name, before_op=pattern.out_op
      )
      pattern.out_op.enclosing_block.replace_uses_of_var_after_op(
          anchor_op=pattern.out_op, old_var=out_var, new_var=x
      )
      # Remove all the ops at once
      pattern.block.remove_ops(pattern.op_list())
  
imports: import numpy
  from coremltools.converters.mil.experimental.passes.generic_pass_infrastructure import fuse_all_blocks
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil import get_new_symbol
  from coremltools.converters.mil.mil.passes.graph_pass import AbstractGraphPass
  from coremltools.converters.mil.mil.passes.helper import _check_child_op_type, _check_var_scalar_value, _check_var_scalar_value_in_interval, block_context_manager
  from coremltools.converters.mil.mil.passes.pass_registry import register_pass
label_desc: Use image node_id 2 for calling the FusePreluPattern2 obj's underlying member method code with example usage: obj.transform_pattern(pattern) without return types