// CHECK-SAME: %[[input_0]],
// CHECK-SAME: %[[input_1]], %[[input_2]], %[[input_3]], %[[input_4]],
// CHECK-SAME: %[[input_5]], %[[input_6]], %[[input_7]], %[[input_8]],
// CHECK-SAME: %[[input_9]], %[[input_9]], %[[input_9]],
// CHECK-SAME: %[[input_10]], %[[input_11]], %[[input_12]], %[[input_13]],
// CHECK-SAME: %[[input_9]], %[[input_9]],
// CHECK-SAME: %[[input_14]], %[[input_15]],

  std::vector<TFE_TensorHandle*> compute_fn_inputs;
  TFE_TensorHandle* input_a = TestScalarTensorHandle(ctx, 2.0f);
  TFE_TensorHandle* input_b = TestScalarTensorHandle(ctx, 1.0f);
  compute_fn_inputs.push_back(input_a);
  compute_fn_inputs.push_back(input_b);

  TFE_Op* compute_fn_op = TF_ConcreteFunctionMakeCallOp(

  // TFL lstm only supports time-majored inputs, so if it's not time-majored,
  // we will transpose the inputs and outputs.
  auto time_major_attr = func_op->getAttrOfType<BoolAttr>("tf.time_major");
  if (time_major_attr == nullptr) return failure();

  bool time_majored = time_major_attr.getValue();
  auto input_type = mlir::dyn_cast_or_null<RankedTensorType>(input.getType());
  if (!input_type) {

}

// CHECK-LABEL: func @tensor_array_while_test
// CHECK-SAME: ([[in_chain:%.*]]: !tfrt.chain
func.func @tensor_array_while_test(%indices: tensor<?xi32>, %input_0: tensor<?x?x?xf32>, %input_1: tensor<?x?x?xf32>) -> (tensor<?x?x512xf32>, tensor<?x?x512xf32>) {
  %index = "tf.Const"() {device = "/device:CPU:0", value = dense<0> : tensor<i32>} : () -> (tensor<i32>)

        },
        "enable": true,
        "hide": true,
        "iconColor": "rgba(0, 211, 255, 1)",
        "name": "Annotations & Alerts",
        "type": "dashboard"
      }
    ]
  },
  "__inputs": [
    {
      "name": "DS_PROMETHEUS",
      "label": "Prometheus",
      "description": "",
      "type": "datasource",
      "pluginId": "prometheus",
      "pluginName": "Prometheus"
    }
  ],

  kQuantizationOp,  // Quantization ops have input/output attr.
};

// For each op type, the following axis carries axis information:
// kDynamicRangeOp: rhs_quantization_axis will carry axis information.
// kUnaryOp: quantization_axis will carry axis information.
// kBinaryOp: Among {lhs, rhs, output}_quantization_axis, only check rhs.
// kQuantizationOp: Among {input, output}_quantization_axis, only check input.

  // CHECK-SAME: %[[INPUT1:.*]]: tensor<1024x3xf32>, %[[INPUT2:.*]]: tensor<1024x3xf32>
  // CHECK: %[[CONSTANT2:.*]] = stablehlo.constant dense<1.000000e+03> : tensor<1024x3xf32>
  // CHECK: %[[ADD:.*]] = stablehlo.add %[[INPUT1]], %[[CONSTANT2]] : tensor<1024x3xf32>
  // CHECK: %[[MUL:.*]] = stablehlo.multiply %[[INPUT1]], %[[INPUT2]] : tensor<1024x3xf32>
  // CHECK: return %[[ADD]], %[[MUL]]

  // CHECK-DAG: %[[INPUT:.*]] = mhlo.bitcast_convert %arg0 : (tensor<3x2xi32>) -> tensor<3x2x!quant.uniform<i32:f32:1, {2.000000e+00:4,2.000000e+00:4}>>
  // CHECK-DAG: %[[MAX:.*]] = mhlo.bitcast_convert %arg1 : (tensor<3x2xi32>) -> tensor<3x2x!quant.uniform<i32:f32:1, {2.000000e+00:4,2.000000e+00:4}>>
  // CHECK-DAG: %[[INPUT_1:.*]] = chlo.broadcast_maximum