code: def gpt2_extract_16():
      metatable = {"relay.Constant": gpt2_extract_consts("float16")}
      mod = tvm.relay.parse(
          """
          #[version = "0.0.5"]
          def @main(%x: Tensor[(1600, 768), float16]) -> Tensor[(50, 32, 3072), float16] {
              %46 = nn.dense(%x, meta[relay.Constant][0], units=768);
              %47 = add(%46, meta[relay.Constant][1]);
              %48 = reshape(%47, newshape=[50, 32, 768]);
              %49 = add(meta[relay.Constant][6], %48);
              %50 = mean(%49, axis=[-1], keepdims=True);
              %51 = subtract(%49, %50);
              %52 = power(%51, 2f16);
              %53 = mean(%52, axis=[-1], keepdims=True);
              %54 = add(%53, 1e-05f16);
              %55 = sqrt(%54);
              %56 = divide(%51, %55);
              %57 = multiply(%56, meta[relay.Constant][2]);
              %58 = add(%57, meta[relay.Constant][3]);
              %59 = reshape(%58, newshape=[-1, 768]);
              %60 = nn.dense(%59, meta[relay.Constant][4], units=3072);
              %61 = add(%60, meta[relay.Constant][5]);
              %62 = reshape(%61, newshape=[50, 32, 3072]);
              %63 = power(%62, 3f16);
              %64 = multiply(%63, 0.044715f16);
              %65 = add(%62, %64);
              %66 = multiply(%65, 0.797885f16);
              %67 = tanh(%66);
              %68 = multiply(%62, 0.5f16);
              %69 = add(%67, 1f16);
              %70 = multiply(%68, %69);
              %70
          }
          """,
          "from_string",
          None,
          metatable,
      )
      return {
          "name": "gpt2_extract_16",
          "input_shapes": {"x": [1600, 768]},
          "input_dtypes": {"x": "float16"},
          "mod": mod,
          "params": None,
          "main_dtype": "float16",
      }
  
imports: import tvm
  import onnx
  import numpy
  import logging
  import tvm.contrib.target.onnx
label_desc: Use image node_id 10 for calling a global function with example usage: gpt2_extract_16() and returns: dict