node {
  name: "tensors/normalize_tensors/component_0"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_INT32
        tensor_shape {
          dim {
            size: 3
          }
        }
        tensor_content: "\000\000\000\000\001\000\000\000\002\000\000\000"
      }

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

  %0 = "tf.AddV2"(%arg0, %arg0) : (tensor<3x2xf32>, tensor<3x2xf32>) -> tensor<3x2xf32>
  func.return %0 : tensor<3x2xf32>

// CHECK: return %[[RES0]] : tensor<256x32x32x16xf32>

{
  "version": 3,
  "operator_codes": [
    {
      "builtin_code": "CONV_2D"
    }
  ],
  "subgraphs": [
    {
      "tensors": [
        {
          "shape": [
            256,
            32,

    SameOperandsAndResultsScale]> {
  let summary = "Packs a list of tensors along a dimension into one tensor";

  let description = [{
    Packs a list of `values_count` rank-`R` tensors into one rank-`(R+1)`
    tensor.

    Packs the `values_count` tensors in `values` into a tensor with rank one
    higher than each tensor in `values`, by packing them along the `axis`
    dimension.

  }];

  let arguments = (ins
    Arg<TF_Int32Tensor, [{int32 `Tensor`.}]>:$arr,
    Arg<TF_Int32Tensor, [{non-negative int32 scalar `Tensor`.}]>:$size,
    Arg<TensorOf<[TF_Float32, TF_Float64, TF_Int32, TF_Int64]>, [{is an int32, int64, float32, or float64 `Tensor` with the same
shape as `arr`, or a length-0 `Tensor`, in which case it acts as all weights
equal to 1.}]>:$weights
  );

// CHECK-NEXT:    buffer: 3
// CHECK-NEXT:  } ],
// CHECK-NEXT:  signature_defs: [  ]
// CHECK-NEXT:}
  %0 = "tfl.add"(%arg0, %arg0) {fused_activation_function = "NONE"} : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
  return %0 : tensor<*xf32>
}

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string - | FileCheck %s

func.func @main(tensor<8xi32>, tensor<8xi32>, tensor<i32>) -> tensor<8xi32> {
^bb0(%arg0: tensor<8xi32>, %arg1: tensor<8xi32>, %arg2: tensor<i32>):
// CHECK: {
// CHECK-NEXT:   version: 3,
// CHECK-NEXT:   operator_codes: [ {
// CHECK-NEXT:     deprecated_builtin_code: 127,
// CHECK-NEXT:     version: 1,
// CHECK-NEXT:     builtin_code: UNSORTED_SEGMENT_PROD

// CHECK-NEXT:  } ]
// CHECK-NEXT:  signature_defs: [ ]
// CHECK-NEXT:}

func.func @main(%arg0: tensor<4xf32>, %arg1: tensor<4x!quant.uniform<u8:f32, 0.1>>) -> tensor<4xf32> {
  "tfl.NumericVerify"(%arg1, %arg0) {tolerance = 0.1 : f32} : (tensor<4x!quant.uniform<u8:f32, 0.1>>, tensor<4xf32>) -> (tensor<4xf32>)
  func.return %arg0 : tensor<4xf32>

func.func @main() -> tensor<3x2xi32> {
  %0 = "tfl.pseudo_const" () {value = dense<0> : tensor<3x2xi32>, tfl.is_variable} : () -> tensor<3x2xi32> loc("variable")
  func.return %0 : tensor<3x2xi32>
}

// CHECK:      {
// CHECK-NEXT:     version: 3,
// CHECK-NEXT:     operator_codes: [ ],
// CHECK-NEXT:     subgraphs: [ {
// CHECK-NEXT:       tensors: [ {
// CHECK-NEXT:         shape: [ 3, 2 ],

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -emit-select-tf-ops -o - | flatbuffer_to_string - | FileCheck %s

func.func @main(tensor<4xcomplex<f64>>, tensor<4xcomplex<f64>>) -> tensor<4xcomplex<f64>> {
^bb0(%arg0: tensor<4xcomplex<f64>>, %arg1: tensor<4xcomplex<f64>>):
// CHECK:  {
// CHECK-NEXT:  version: 3,
// CHECK-NEXT:  operator_codes: [ {
// CHECK-NEXT:    deprecated_builtin_code: 32,
// CHECK-NEXT:    custom_code: "FlexAdd",