code: def resnext50_32x4d_16():
      metatable = {"relay.Constant": resnext50_32x4d_consts("float16")}
      mod = tvm.relay.parse(
          """
          #[version = "0.0.5"]
          def @main(%x: Tensor[(1, 64, 56, 56), float16]) {
            %0 = nn.conv2d(%x, meta[relay.Constant][0], padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]);
            %1 = nn.relu(%0);
            %2 = nn.conv2d(%1, meta[relay.Constant][1], padding=[1, 1, 1, 1], groups=32, channels=128, kernel_size=[3, 3]);
            %3 = nn.relu(%2);
            %4 = nn.conv2d(%3, meta[relay.Constant][2], padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]);
            %5 = nn.conv2d(%x, meta[relay.Constant][3], padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]);
            %6 = add(%4, %5);
            %7 = nn.relu(%6);
            %8 = nn.conv2d(%7, meta[relay.Constant][4], padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]);
            %9 = nn.relu(%8);
            %10 = nn.conv2d(%9, meta[relay.Constant][5], padding=[1, 1, 1, 1], groups=32, channels=128, kernel_size=[3, 3]);
            %11 = nn.relu(%10);
            %12 = nn.conv2d(%11, meta[relay.Constant][6], padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]);
            %13 = add(%12, %7);
            %14 = nn.relu(%13);
            %15 = nn.conv2d(%14, meta[relay.Constant][7], padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]);
            %16 = nn.relu(%15);
            %17 = nn.conv2d(%16, meta[relay.Constant][8], padding=[1, 1, 1, 1], groups=32, channels=128, kernel_size=[3, 3]);
            %18 = nn.relu(%17);
            %19 = nn.conv2d(%18, meta[relay.Constant][9], padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]);
            %20 = add(%19, %14);
            %21 = nn.relu(%20);
            %22 = nn.conv2d(%21, meta[relay.Constant][10], padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]);
            %23 = nn.relu(%22);
            %24 = nn.conv2d(%23, meta[relay.Constant][11], strides=[2, 2], padding=[1, 1, 1, 1], groups=32, channels=256, kernel_size=[3, 3]);
            %25 = nn.relu(%24);
            %26 = nn.conv2d(%25, meta[relay.Constant][12], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]);
            %27 = nn.conv2d(%21, meta[relay.Constant][13], strides=[2, 2], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]);
            %28 = add(%26, %27);
            %29 = nn.relu(%28);
            %30 = nn.conv2d(%29, meta[relay.Constant][14], padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]);
            %31 = nn.relu(%30);
            %32 = nn.conv2d(%31, meta[relay.Constant][15], padding=[1, 1, 1, 1], groups=32, channels=256, kernel_size=[3, 3]);
            %33 = nn.relu(%32);
            %34 = nn.conv2d(%33, meta[relay.Constant][16], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]);
            %35 = add(%34, %29);
            %36 = nn.relu(%35);
            %37 = nn.conv2d(%36, meta[relay.Constant][17], padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]);
            %38 = nn.relu(%37);
            %39 = nn.conv2d(%38, meta[relay.Constant][18], padding=[1, 1, 1, 1], groups=32, channels=256, kernel_size=[3, 3]);
            %40 = nn.relu(%39);
            %41 = nn.conv2d(%40, meta[relay.Constant][19], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]);
            %42 = add(%41, %36);
            %43 = nn.relu(%42);
            %44 = nn.conv2d(%43, meta[relay.Constant][20], padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]);
            %45 = nn.relu(%44);
            %46 = nn.conv2d(%45, meta[relay.Constant][21], padding=[1, 1, 1, 1], groups=32, channels=256, kernel_size=[3, 3]);
            %47 = nn.relu(%46);
            %48 = nn.conv2d(%47, meta[relay.Constant][22], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]);
            %49 = add(%48, %43);
            %50 = nn.relu(%49);
            %51 = nn.conv2d(%50, meta[relay.Constant][23], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]);
            %52 = nn.relu(%51);
            %53 = nn.conv2d(%52, meta[relay.Constant][24], strides=[2, 2], padding=[1, 1, 1, 1], groups=32, channels=512, kernel_size=[3, 3]);
            %54 = nn.relu(%53);
            %55 = nn.conv2d(%54, meta[relay.Constant][25], padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]);
            %56 = nn.conv2d(%50, meta[relay.Constant][26], strides=[2, 2], padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]);
            %57 = add(%55, %56);
            %58 = nn.relu(%57);
            %59 = nn.conv2d(%58, meta[relay.Constant][27], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]);
            %60 = nn.relu(%59);
            %61 = nn.conv2d(%60, meta[relay.Constant][28], padding=[1, 1, 1, 1], groups=32, channels=512, kernel_size=[3, 3]);
            %62 = nn.relu(%61);
            %63 = nn.conv2d(%62, meta[relay.Constant][29], padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]);
            %64 = add(%63, %58);
            %65 = nn.relu(%64);
            %66 = nn.conv2d(%65, meta[relay.Constant][30], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]);
            %67 = nn.relu(%66);
            %68 = nn.conv2d(%67, meta[relay.Constant][31], padding=[1, 1, 1, 1], groups=32, channels=512, kernel_size=[3, 3]);
            %69 = nn.relu(%68);
            %70 = nn.conv2d(%69, meta[relay.Constant][32], padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]);
            %71 = add(%70, %65);
            %72 = nn.relu(%71);
            %73 = nn.conv2d(%72, meta[relay.Constant][33], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]);
            %74 = nn.relu(%73);
            %75 = nn.conv2d(%74, meta[relay.Constant][34], padding=[1, 1, 1, 1], groups=32, channels=512, kernel_size=[3, 3]);
            %76 = nn.relu(%75);
            %77 = nn.conv2d(%76, meta[relay.Constant][35], padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]);
            %78 = add(%77, %72);
            %79 = nn.relu(%78);
            %80 = nn.conv2d(%79, meta[relay.Constant][36], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]);
            %81 = nn.relu(%80);
            %82 = nn.conv2d(%81, meta[relay.Constant][37], padding=[1, 1, 1, 1], groups=32, channels=512, kernel_size=[3, 3]);
            %83 = nn.relu(%82);
            %84 = nn.conv2d(%83, meta[relay.Constant][38], padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]);
            %85 = add(%84, %79);
            %86 = nn.relu(%85);
            %87 = nn.conv2d(%86, meta[relay.Constant][39], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]);
            %88 = nn.relu(%87);
            %89 = nn.conv2d(%88, meta[relay.Constant][40], padding=[1, 1, 1, 1], groups=32, channels=512, kernel_size=[3, 3]);
            %90 = nn.relu(%89);
            %91 = nn.conv2d(%90, meta[relay.Constant][41], padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]);
            %92 = add(%91, %86);
            %93 = nn.relu(%92);
            %94 = nn.conv2d(%93, meta[relay.Constant][42], padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]);
            %95 = nn.relu(%94);
            %96 = nn.conv2d(%95, meta[relay.Constant][43], strides=[2, 2], padding=[1, 1, 1, 1], groups=32, channels=1024, kernel_size=[3, 3]);
            %97 = nn.relu(%96);
            %98 = nn.conv2d(%97, meta[relay.Constant][44], padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1]);
            %99 = nn.conv2d(%93, meta[relay.Constant][45], strides=[2, 2], padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1]);
            %100 = add(%98, %99);
            %101 = nn.relu(%100);
            %102 = nn.conv2d(%101, meta[relay.Constant][46], padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]);
            %103 = nn.relu(%102);
            %104 = nn.conv2d(%103, meta[relay.Constant][47], padding=[1, 1, 1, 1], groups=32, channels=1024, kernel_size=[3, 3]);
            %105 = nn.relu(%104);
            %106 = nn.conv2d(%105, meta[relay.Constant][48], padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1]);
            %107 = add(%106, %101);
            %108 = nn.relu(%107);
            %109 = nn.conv2d(%108, meta[relay.Constant][49], padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]);
            %110 = nn.relu(%109);
            %111 = nn.conv2d(%110, meta[relay.Constant][50], padding=[1, 1, 1, 1], groups=32, channels=1024, kernel_size=[3, 3]);
            %112 = nn.relu(%111);
            %113 = nn.conv2d(%112, meta[relay.Constant][51], padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1]);
            %114 = add(%113, %108);
            nn.relu(%114)
          }
          """,
          "from_string",
          None,
          metatable,
      )
      return {
          "name": "resnext50_32x4d_16",
          "input_shapes": {"x": [1, 64, 56, 56]},
          "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 22 for calling a global function with example usage: resnext50_32x4d_16() and returns: dict