// CHECK-LABEL: @xla_svd
  func.func @xla_svd(%arg0: tensor<1x1xf32>) -> (tensor<1xf32>, tensor<1x1xf32>, tensor<1x1xf32>) {
    // CHECK-NOT: XlaSvd
    %s, %u, %v = "tf.XlaSvd"(%arg0) {max_iter = 1, epsilon = 1.0E-09 : f32, precision_config = ""} : (tensor<1x1xf32>) -> (tensor<1xf32>, tensor<1x1xf32>, tensor<1x1xf32>)
    func.return %s, %u, %v : tensor<1xf32>, tensor<1x1xf32>, tensor<1x1xf32>
  }

  func.func @identity(%arg0: f32) -> f32 {

    // CHECK: (tensor<!tf_type.string>, tensor<1x!tf_type.string>) -> tensor<128xf32>
    %3 = "tf.Restore"(%arg0, %cst_0) {device = ""} : (tensor<!tf_type.string>, tensor<1x!tf_type.string>) -> tensor<*xf32>
    "tf.AssignVariableOp"(%2, %3) : (tensor<!tf_type.resource<tensor<128xf32>>>, tensor<*xf32>) -> ()

  // CHECK: "tfl.mean"(%arg0, %arg1) <{keep_dims = false}>
  %0 = "tfl.mean"(%arg0, %arg1) {keep_dims = false}: (tensor<2x2xf32>, tensor<1xi32>) -> tensor<1x2xf32>
  func.return %0 : tensor<1x2xf32>
}

// -----

// CHECK-LABEL: testMean_true
func.func @testMean_true(%arg0: tensor<2x2xf32>, %arg1 : tensor<1xi32>) -> tensor<1x2xf32> {

  // CHECK:  %4 = "tfl.reduce_max"(%arg0, %cst) <{keep_dims = false}> : (tensor<1x32x1xf32>, tensor<1xi32>) -> tensor<1x1xf32>
  // CHECK:  %5 = "tfl.arg_max"(%arg0, %cst) : (tensor<1x32x1xf32>, tensor<1xi32>) -> tensor<1x1xi32>
  // CHECK:  return %4, %5 : tensor<1x1xf32>, tensor<1x1xi32>
}

// CHECK-LABEL:   func @convert_pytorch_argmax
func.func @convert_pytorch_argmax(%arg0: tensor<1x9xi32>) -> tensor<1xi32> {

  // CHECK-DAG: %[[SEL:.*]] = mhlo.select %[[COMP]], %[[RS]], %[[ZERO_MAT]] : tensor<12x12xi1>, tensor<12x12xf32>
  // CHECK-DAG: %[[RED:.*]] = mhlo.reduce(%[[SEL]] init: %[[ZERO]]) applies mhlo.add across dimensions = [0] : (tensor<12x12xf32>, tensor<f32>) -> tensor<12xf32>

// CHECK:   [[ZERO:%.*]] = arith.constant dense<0> : tensor<i32>
// CHECK:   [[EXP_ITEM:%.*]] = "tf.ExpandDims"([[ITEM]], [[ZERO]]) {{.*}} -> tensor<1x10xf32>
// CHECK:   [[RESULT:%.*]] = "tf.Concat"(%cst, [[INPUT]], [[EXP_ITEM]]) : {{.*}} -> tensor<?x10xf32>
// CHECK:   return [[RESULT]] : tensor<?x10xf32>
}

// -----

// -----

// CHECK-LABEL: func @selectV2BroadcastAll
func.func @selectV2BroadcastAll(%arg0: tensor<8x1x1xi1>, %arg1: tensor<1x8x1xi32>, %arg2: tensor<1x1x8xi32>) -> tensor<8x8x8xi32> {
  %0 = "tf.SelectV2"(%arg0, %arg1, %arg2) : (tensor<8x1x1xi1>, tensor<1x8x1xi32>, tensor<1x1x8xi32>) -> tensor<8x8x8xi32>
  func.return %0: tensor<8x8x8xi32>
}

// -----

// CHECK-LABEL: func @selectV2DynamicRanked