// CHECK-NEXT:     } ],
  // CHECK-NEXT:     metadata: [ {
  // CHECK-NEXT:       name: "min_runtime_version",
  // CHECK-NEXT:       buffer: 4
  // CHECK-NEXT:     } ],
  // CHECK-NEXT:     signature_defs: [  ]
  // CHECK-NEXT: }
  // CHECK-EMPTY:

  %0 = "tfl.pseudo_const" () {value = dense<[[-5.0, 10000.0], [150.0, 10.0], [5.0, 100.0]]> : tensor<3x2xf32>} : () -> tensor<3x2xf32> loc("Const")

      self.assertCountEqual(
          output_meta_graph_def.signature_def.keys(), signature_def_keys
      )

      signature_def = output_meta_graph_def.signature_def[signature_def_key]

      input_tensor_name = signature_def.inputs['input_vocabs'].name
      input_tensor = sess.graph.get_tensor_by_name(input_tensor_name)

      lookup_tensor_name = signature_def.outputs['lookup'].name

// CHECK-NEXT:  } ],
// CHECK-NEXT:  metadata: [ {
// CHECK-NEXT:    name: "min_runtime_version",
// CHECK-NEXT:    buffer: 5
// CHECK-NEXT:  } ]
// CHECK-NEXT:  signature_defs: [ ]
// CHECK-NEXT:}

  %cst = "tfl.no_value"() {value = unit} : () -> none

  // CHECK-NEXT:   } ],
  // CHECK-NEXT:   metadata: [ {
  // CHECK-NEXT:   name: "min_runtime_version",
  // CHECK-NEXT:   buffer: 3
  // CHECK-NEXT:   } ]
  // CHECK-NEXT:   signature_defs: [ ]
  // CHECK-NEXT: }

# CHECK-NEXT:   } ],
# CHECK-NEXT:   metadata: [ {
# CHECK-NEXT:   name: "min_runtime_version",
# CHECK-NEXT:   buffer: 4
# CHECK-NEXT:   } ],
# CHECK-NEXT:   signature_defs: [ ]

// CHECK-NEXT:     } ],
// CHECK-NEXT:     metadata: [ {
// CHECK-NEXT:     name: "min_runtime_version",
// CHECK-NEXT:     buffer: 7
// CHECK-NEXT:     } ]
// CHECK-NEXT:     signature_defs: [ ]
// CHECK-NEXT:   }
// CHECK-EMPTY:

^bb0(%arg0: tensor<4 x f32>, %arg1: tensor<4 x i8>, %arg2: tensor<4 x f32>, %arg3: tensor<4 x f32>):

    ValidateAssets(export_dir, bundle);
    // Retrieve the regression signature from meta graph def.
    const auto& signature_def = bundle.GetSignatures().at("regress_x_to_y");

    const string input_name = signature_def.inputs().at(kRegressInputs).name();
    const string output_name =
        signature_def.outputs().at(kRegressOutputs).name();

    std::vector<tstring> serialized_examples;
    for (float x : {0, 1, 2, 3}) {

  // CHECK-NEXT:  } ],
  // CHECK-NEXT:  metadata: [ {
  // CHECK-NEXT:  name: "min_runtime_version",
  // CHECK-NEXT:  buffer: 5
  // CHECK-NEXT:  } ]
  // CHECK-NEXT:  signature_defs: [ ]
  // CHECK-NEXT:}

  %cst = "tfl.no_value"() {value = unit} : () -> none

  // CHECK-NEXT:  } ],
  // CHECK-NEXT:  metadata: [ {
  // CHECK-NEXT:  name: "min_runtime_version",
  // CHECK-NEXT:  buffer: 5
  // CHECK-NEXT:  } ]
  // CHECK-NEXT:  signature_defs: [ ]
  // CHECK-NEXT:}

  %cst = "tfl.no_value"() {value = unit} : () -> none

        value: "\n\003a:0\022\010a/Assign\032\010a/read:0"
        value: "\n\003b:0\022\010b/Assign\032\010b/read:0"
        value: "\n\003c:0\022\010c/Assign\032\010c/read:0"
      }
    }
  }
  signature_def {
    key: "classify_x2_to_y3"
    value {
      inputs {
        key: "inputs"
        value {
          name: "x2:0"
          dtype: DT_FLOAT
          tensor_shape {
            dim {