// CHECK: "tf.BatchMatMulV2"(%arg0, %arg1) <{adj_x = false, adj_y = false, grad_x = false, grad_y = false}> {device = "/job:localhost/replica:0/task:0/device:GPU:0"}
  %0 = "tf.BatchMatMul"(%arg0, %arg1) <{adj_x = false, adj_y = false}> {device = "/job:localhost/replica:0/task:0/device:GPU:0"} : (tensor<2x3x5xf32>, tensor<2x5x7xf32>) -> tensor<2x3x7xf32>

}

func.func @strided_slice_big_dims(%arg0: tensor<5x6x7xf32>, %arg1: tensor<3xi32>, %arg2: tensor<3xi32>, %arg3: tensor<3xi32>) -> tensor<1x1x5x6x7xf32> {
  %0 = "tf.StridedSlice"(%arg0, %arg1, %arg2, %arg3) {begin_mask = 0 : i64, ellipsis_mask = 0 : i64, end_mask = 0 : i64, new_axis_mask = 7 : i64, shrink_axis_mask = 0 : i64, offset = false} : (tensor<5x6x7xf32>, tensor<3xi32>, tensor<3xi32>, tensor<3xi32>) -> tensor<1x1x5x6x7xf32>

func.func @testBatchMatMul(%lhs: tensor<2x?x2x?x3x5xf32>, %rhs: tensor<2x2x?x?x5x7xf32>) {
  %0 = "tf.BatchMatMul"(%lhs, %rhs) : (tensor<2x?x2x?x3x5xf32>, tensor<2x2x?x?x5x7xf32>) -> tensor<2x?x?x?x3x7xf32>
  func.return
}

// -----

// Mismatching batch dimensions.
func.func @testBatchMatMul(%lhs: tensor<1x3x5xf32>, %rhs: tensor<2x5x7xf32>) {

    return %1 : tensor<*xf32>
  }

  // CHECK-LABEL: func @gather_concat_reshape
  func.func @gather_concat_reshape(%arg0: tensor<?x1x7xf32>, %arg1: tensor<?x7xf32>) -> tensor<*xf32> {
    %0 = "tf.Shape"(%arg0) : (tensor<?x1x7xf32>) -> tensor<3xi32>

    %indices = "tf.Const"() {value = dense<[0, 1]> : tensor<2xi32>} : () -> tensor<2xi32>

}

// -----

func.func @unpack(%arg0: tensor<?x2x5x3xf32>) -> () {
  %0:2 = "tfl.unpack"(%arg0) {axis = 1 : i32, num = 2 : i32} : (tensor<?x2x5x3xf32>) -> (tensor<5x5x3xf32>, tensor<5x5x3xf32>)
  func.return
}

// -----

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