// CHECK: %[[ADD_0:.+]] = stablehlo.add %[[DOT_GENERAL_0]], %[[DYNAMIC_BROADCAST_IN_DIM_0]] : tensor<?x3x!quant.uniform<i32:f32:1, {{.*}}>>
// CHECK: %[[UNIFORM_QUANTIZE_1:.+]] = stablehlo.uniform_quantize %[[ADD_0]] : (tensor<?x3x!quant.uniform<i32:f32:1, {{.*}}>>)

      func.func @main() -> tensor<!tf_type.variant<tensor<64x1xbf16>>> {
      // unknown rank
      %elem_shape = "tf.Const"() <{value = dense<-1> : tensor<i32>}> {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : () -> tensor<i32>
      // zero reserved elements

module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
    func.func @main(%arg0 : tensor<?xf32>) -> tensor<?xf32> {
      %scales = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32>
      %zps = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32>

      %0 = "tf.UniformQuantize"(%arg0, %scales, %zps) {

  %1 = "tfl.max_pool_2d"(%0) {filter_height = 1 : i32, filter_width = 1 : i32, fused_activation_function = "RELU6", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32} : (tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32>
  func.return %1 : tensor<1x1x1x16xf32>

// MIN-MAX-CHECK-NEXT:  [[lhs:%.*]], {{.*}}, {{.*}}, {{.*}} = "tf.CustomAggregator"(%arg0) <{calibration_method = 1 : i32, id = "composite_conv2d_with_relu6_fn_arg_0_calibration_method_1",...

// CHECK:           %[[VAL_6:.*]] = "tf.Minimum"(%[[VAL_4]], %[[VAL_5]]) : (tensor<i32>, tensor<i32>) -> tensor<i32>
// CHECK:           %[[VAL_7:.*]] = "tf.Maximum"(%[[VAL_6]], %[[VAL_3]]) : (tensor<i32>, tensor<i32>) -> tensor<i32>
// CHECK:           %[[VAL_8:.*]] = "tf.Cast"(%[[VAL_2]]) <{Truncate = false}> : (tensor<i32>) -> tensor<i32>
// CHECK:           %[[VAL_9:.*]] = "tf.Const"() <{value = dense<1> : tensor<i32>}> : () -> tensor<i32>

}

func.func @argmin(%arg0: tensor<3xi32>, %arg1: tensor<i32>) -> tensor<i32> {
  %0 = "tf.ArgMin"(%arg0, %arg1) : (tensor<3xi32>, tensor<i32>) -> tensor<i32>
  func.return %0 : tensor<i32>

// CHECK-LABEL: argmin
// CHECK:  "tfl.arg_min"(%arg0, %arg1) : (tensor<3xi32>, tensor<i32>) -> tensor<i32>
}

func.func @sigmoid(%arg0: tensor<?x88xf32>) -> tensor<?x88xf32> {

  }];

  let arguments = (ins
    TFL_TensorOf<[F32, I16, I32, I64, I8, UI8, QI8, QUI8, QI16]>:$value,
    TFL_1DTensorOf<[I32], [I32]>:$size_splits,
    TFL_0DTensorOf<[I32], [I32]>:$split_dim,
    ConfinedAttr<I32Attr, [IntPositive]>:$num_splits
  );

  let results = (outs
    TFL_VariadicTensorOf<[F32, I16, I32, I64, I8, UI8, QI8, QUI8, QI16]>:$outputs
  );

  let hasVerifier = 1;

    // CHECK: %[[RES_READ_VAL:[0-9]*]] = "tf.ReadVariableOp"
    // CHECK: "tf.Sub"(%[[RES_READ_VAL]], %[[ONE]])
    // CHECK: "tf.AssignVariableOp"

    %1 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
    "tf.AssignSubVariableOp"(%0, %1) {dtype = "tfdtype$DT_INT32"} : (tensor<!tf_type.resource<tensor<i32>>>, tensor<i32>) -> ()

    tf_device.return

// CHECK: %[[CONV2D:.+]] = "tfl.conv_2d"(%[[ARG]], %[[QCONST_0]], %[[QCONST_1]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32}> : (tensor<1x3x3x4x!quant.uniform<i8:f32, 3.000000e+00:-100>>,...