code: def _tmp_strategy(attrs, inputs, out_type, target):
      strategy = relay.op.OpStrategy()
      strategy.add_implementation(
          relay.op.strategy.wrap_compute_conv2d(_compute_conv2d_1),
          relay.op.strategy.wrap_topi_schedule(_schedule_conv2d_1),
          name="conv2d_1",
          plevel=10,
      )
      strategy.add_implementation(
          relay.op.strategy.wrap_compute_conv2d(_compute_conv2d_2),
          relay.op.strategy.wrap_topi_schedule(_schedule_conv2d_2),
          name="conv2d_2",
          plevel=15,
      )
      ic = inputs[0].shape[1]
      with tvm.te.SpecializedCondition(ic >= 16):
          strategy.add_implementation(
              relay.op.strategy.wrap_compute_conv2d(_compute_conv2d_3),
              relay.op.strategy.wrap_topi_schedule(_schedule_conv2d_3),
              name="conv2d_3",
              plevel=20,
          )
      return strategy
  
imports: import numpy
  import tvm
  from tvm import te
  import tvm.testing
  from tvm import relay
  from tvm import autotvm
  from tvm import topi
  from tvm.relay.backend import te_compiler
  from tvm.relay.testing import run_infer_type
  from tvm.relay.testing.temp_op_attr import TempOpAttr
label_desc: Use image node_id 7 for calling a global function with example usage: _tmp_strategy(attrs, inputs, out_type, target) and returns: strategy