// time constant.
  %0 = "tf.ConcatV2"(%arg0, %arg0, %arg0, %arg0, %axis) : (tensor<16x1xf32>, tensor<16x1xf32>, tensor<16x1xf32>, tensor<16x1xf32>, tensor<i32>) -> tensor<16x4xf32>

  // Just to introduce an extra use for %cst.
  %1 = "tf.AddV2"(%axis, %axis) {device = ""} : (tensor<i32>, tensor<i32>) -> tensor<i32>

  return %0 : tensor<16x4xf32>
}
// Check that the constant is cloned with same value.

// CHECK-SAME: (%[[ARG_0:[a-z0-9]*]]: tensor<!tf_type.resource<tensor<10x3xf32>>>, %[[ARG_1:[a-z0-9]*]]: tensor<!tf_type.resource<tensor<10x3xf32>>>, %[[ARG_2:[a-z0-9]*]]: tensor<!tf_type.resource<tensor<10x3xf32>>>, %[[ARG_3:[a-z0-9]*]]: tensor<!tf_type.resource<tensor<10x3xf32>>>)
!rtype = tensor<!tf_type.resource<tensor<10x3xf32>>>

    %5 = stablehlo.constant dense<2.000000e+02> : tensor<1x1xf32>  // Output inverse scale.
    %6 = stablehlo.constant dense<2> : tensor<1x1xi8>  // Output zero point.
    %7 = call @uniform_quantize_0(%arg0, %0, %1) : (tensor<1x2xf32>, tensor<1x1xf32>, tensor<1x1xi8>) -> tensor<1x2xi8>
    %8 = stablehlo.convert %7 : (tensor<1x2xi8>) -> tensor<1x2xf32>

      %2 = "tf.ReadVariableOp"(%arg15) : (tensor<*x!tf_type.resource<tensor<7x7x3x64xf32>>>) -> tensor<7x7x3x64xf32>
      %3 = "tf.ReadVariableOp"(%arg16) : (tensor<*x!tf_type.resource<tensor<1001xf32>>>) -> tensor<1001xf32>
      %4 = "tf.ReadVariableOp"(%arg17) : (tensor<*x!tf_type.resource<tensor<64x1001xf32>>>) -> tensor<64x1001xf32>
      %5 = "tf.ReadVariableOp"(%arg18) : (tensor<*x!tf_type.resource<tensor<f32>>>) -> tensor<f32>

func.func @QuantizedCatsAddRequantsTest(%arg0: tensor<1x1xf32>, %arg1: tensor<1x1xf32>, %arg2: tensor<1x1xf32>, %arg3: tensor<1x1xf32>) -> (tensor<1x4xf32>, tensor<1x3xf32>) {
  %0 = "quantfork.stats"(%arg0) {layerStats = dense<[-0.440728068, 0.189515018]> : tensor<2xf32>} : (tensor<1x1xf32>) -> tensor<1x1xf32>
  %1 = "quantfork.stats"(%arg1) {layerStats = dense<[-0.154693216, 0.26483655]> : tensor<2xf32>} : (tensor<1x1xf32>) -> tensor<1x1xf32>

    %cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<10xf32>} : () -> tensor<10xf32>
    %cst_0 = "tf.Const"() {value = dense<[-1, 10]> : tensor<2xi32>} : () -> tensor<2xi32>
    %1 = "tf.MatMul"(%arg0, %arg1) {
      transpose_a = false, transpose_b = false

}

// -----

func.func @testBatchMatMulV2(%lhs: tensor<f32>, %rhs: tensor<10x10xf32>) {
  // expected-error @+1 {{requires lhs operand to have rank at least two}}
  %0 = "tf.BatchMatMulV2"(%lhs, %rhs) : (tensor<f32>, tensor<10x10xf32>) -> tensor<10x10xf32>
}

// -----

func.func @testBatchMatMulV2(%lhs: tensor<10x10xf32>, %rhs: tensor<f32>) {
  // expected-error @+1 {{requires rhs operand to have rank at least two}}

}

// CHECK-LABEL: @broadcast_batch_matmul_v2_lhs
func.func @broadcast_batch_matmul_v2_lhs(%arg0: tensor<17x17xf32>, %arg1: tensor<17x17x24xf32>) -> tensor<17x17x24xf32> {
  %cst = arith.constant dense<[17, 17, 17]> : tensor<3xi64>
  %0 = "tf.BroadcastTo"(%arg0, %cst) : (tensor<17x17xf32>, tensor<3xi64>) -> tensor<17x17x17xf32>

}

// -----

// CHECK-LABEL: listPushBack
func.func @listPushBack(%arg0: tensor<!tf_type.variant<tensor<?x1xf32>>>, %arg1: tensor<16x1xf32>) -> tensor<!tf_type.variant<tensor<?x1xf32>>>  {
  %0 = "tf.TensorListPushBack"(%arg0, %arg1) : (tensor<!tf_type.variant<tensor<?x1xf32>>>, tensor<16x1xf32>) -> tensor<!tf_type.variant<tensor<?x1xf32>>>

    %1 = stablehlo.maximum %0, %cst : tensor<10x1x3xf32>
    return %1 : tensor<10x1x3xf32>
  }
}

// -----

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 1836 : i32}, tf_saved_model.semantics} {