code: def test_fold_dropout():
      def before():
          # A constant graph to fire fold constant
          data = relay.const(np.arange(10).astype(np.float32))
          dropout = relay.nn.dropout(data)
          add = dropout + relay.const(1.0)
          return relay.Function(relay.analysis.free_vars(add), add)
  
      passes = tvm.transform.Sequential(
          [
              relay.transform.InferType(),
              relay.transform.FoldConstant(),
          ]
      )
  
      before_mod = tvm.IRModule.from_expr(before())
  
      with tvm.transform.PassContext(opt_level=3):
          after_mod = passes(before_mod)
  
      tvm.ir.assert_structural_equal(
          run_infer_type(before_mod["main"]), after_mod["main"]
      )
  
imports: import numpy
  import tvm
  import tvm.testing
  from tvm import relay
  from tvm.relay.backend import Executor
  from tvm.relay import transform
  from tvm.relay.build_module import bind_params_by_name
  from tvm.relay.testing import run_infer_type, create_workload
label_desc: Use image node_id 14 for calling a global function with example usage: test_fold_dropout() and returns: relay