// CHECK-SAME: (%[[ARG0:.*]]: tensor<10x8x9xf32>, %[[ARG1:.*]]: tensor<5xi32>, %[[ARG2:.*]]: tensor<5x8x9xf32>) -> tensor<10x8x9xf32>
func.func @main(%arg0: tensor<10x8x9xf32>, %arg1: tensor<5xi32>, %arg2: tensor<5x8x9xf32>) -> tensor<10x8x9xf32> {
  %elem_shape = "tf.Const"() {value = dense<[8, 9]> : tensor<2xi32>} : () -> tensor<2xi32>
  // CHECK: %[[BUFFER:.*]] = "tf.Identity"(%[[ARG0]]) : (tensor<10x8x9xf32>) -> tensor<10x8x9xf32>

  %4 = "tfl.pseudo_const"() {value = dense<0.000000e+00> : tensor<1x4xf32>} : () -> tensor<1x4xf32>
  %5 = "quantfork.stats"(%4) {layerStats = dense<[-56.2916565, 122.922478]> : tensor<2xf32>} : (tensor<1x4xf32>) -> tensor<1x4xf32>
  %6 = "tfl.svdf"(%0, %1, %2, %3, %5) {fused_activation_function = "RELU", rank = 1 : i32} : (tensor<1x3xf32>, tensor<2x3xf32>, tensor<2x1xf32>, tensor<2xf32>, tensor<1x4xf32>) -> tensor<1x2xf32>

//CHECK:func.func private @slice(%arg0: tensor<160x20x1xf32>) -> tensor<1x1x1xf32> {
//CHECK-NEXT: %0 = "vhlo.slice_v1"(%arg0) <{limit_indices = #vhlo.tensor_v1<dense<0> : tensor<3xi64>>, start_indices = #vhlo.tensor_v1<dense<1> : tensor<3xi64>>, strides = #vhlo.tensor_v1<dense<1> : tensor<3xi64>>}> : (tensor<160x20x1xf32>) -> tensor<1x1x1xf32> 
//CHECK-NEXT: return %0 : tensor<1x1x1xf32>
//CHECK-NEXT:}

  %0:3 = "tf.Split"(%arg0, %arg1) : (tensor<i32>, tensor<1x4x3x3xf32>) -> (tensor<1x4x3xf32>, tensor<1x4x3xf32>, tensor<1x4x3xf32>)
  func.return %0#0 : tensor<1x4x3xf32>

  // CHECK-LABEL: split
  // CHECK: "tfl.split"(%arg0, %arg1) <{num_splits = 3 : i32}> : (tensor<i32>, tensor<1x4x3x3xf32>) -> (tensor<1x4x3xf32>, tensor<1x4x3xf32>, tensor<1x4x3xf32>)
}

func.func @leaky_relu_grad(%arg0: tensor<1x4x4xf32>, %arg1: tensor<1x4x4xf32>) -> tensor<1x4x4xf32> attributes {tf.entry_function = {}} {
    // CHECK-NEXT: %[[ALPHA:.*]] = "chlo.constant_like"(%arg1) <{value = 2.000000e-01 : f32}> : (tensor<1x4x4xf32>) -> tensor<1x4x4xf32>
    // CHECK-NEXT: %[[ZERO:.*]] = "chlo.constant_like"(%arg1) <{value = 0.000000e+00 : f32}> : (tensor<1x4x4xf32>) -> tensor<1x4x4xf32>

  // CHECK: [[Result:%.*]] = "tf.Case"(%arg0, %arg1)
  // CHECK-SAME: branches = [@tf.CaseRegion_branch0{{.*}}, @tf.CaseRegion_branch1{{.*}}]
  // CHECK-SAME: is_stateless = false
  %0 = "tf.CaseRegion"(%arg0) ({
    %1 = "tf.Cast"(%arg1) {Truncate = false} : (tensor<!tf_type.resource<tensor<1x2x3xf32>>>) -> tensor<!tf_type.resource>

    %0 = "tf.BatchMatMulV2"(%arg3, %arg1) {adj_x = false, adj_y = false} : (tensor<1x3xf32>, tensor<3x4xf32>) -> tensor<1x4xf32>
    %1 = arith.constant dense<[[2.3, 3.4, 4.5, 5.5]]> : tensor<1x4xf32>
    %2 = "tf.Add"(%0, %1) : (tensor<1x4xf32>, tensor<1x4xf32>) -> tensor<1x4xf32>
    %3 = tensor.cast %2 : tensor<1x4xf32> to tensor<1x?xf32>
    func.return %3 : tensor<1x?xf32>
}

func.func @partitioned_input_rank_mismatch(%arg0: tensor<!tf_type.resource<tensor<1x4x4xf32>>>) {
  // expected-error @+1 {{rank}}
  %0 = "tf.TPUPartitionedInputV2"(%arg0) {_XlaSharding = "\08\03\1A\05\04\01\01\01\02\22\08\00\01\02\03\04\05\06\070\01", partition_dims = [4, 1, 1, 2], is_packed = true} : (tensor<!tf_type.resource<tensor<1x4x4xf32>>>) -> tensor<!tf_type.resource<tensor<4x4x4xf32>>>

  func.func @batchmatmulv2(%arg0: tensor<1x4x2xf32>, %arg1: tensor<3x2x4xf32>) -> tensor<3x4x4xf32> {
    // CHECK: mhlo.reduce
    // CHECK: mhlo.dot_general
    // CHECK: mhlo.transpose
    %0 = "tf.BatchMatMulV2"(%arg0, %arg1) {T = f32, adj_x = false, adj_y = false, grad_x = false, grad_y = false, device = ""} : (tensor<1x4x2xf32>, tensor<3x2x4xf32>) -> tensor<3x4x4xf32>
    func.return %0 : tensor<3x4x4xf32>
  }

func.func @invalidSelect(%arg0: tensor<1x8xi1>, %arg1: tensor<1x8x8xi32>, %arg2: tensor<1x8x8xi32>) -> tensor<1x8x8xi32> {
  // expected-error @+1 {{requires that pred is a scalar OR has the same rank as t and e OR is a vector}}
  %0 = "tf.Select"(%arg0, %arg1, %arg2) : (tensor<1x8xi1>, tensor<1x8x8xi32>, tensor<1x8x8xi32>) -> tensor<1x8x8xi32>
  func.return %0: tensor<1x8x8xi32>
}

// -----