// CHECK: %[[MATMUL_PACKED:.*]] = "tf.Pack"(%[[MATMUL_1]], %[[MATMUL_2]], %[[MATMUL_3]], %[[MATMUL_4]], %[[MATMUL_5]], %[[MATMUL_6]]) <{axis = 0 : i64}> : (tensor<4x6xf32>, tensor<4x6xf32>, tensor<4x6xf32>, tensor<4x6xf32>, tensor<4x6xf32>, tensor<4x6xf32>) -> tensor<6x4x6xf32>
  // CHECK: %[[RESULT:.*]] = "tf.Reshape"(%[[MATMUL_PACKED]], %[[RESULT_SHAPE]]) : (tensor<6x4x6xf32>, tensor<4xi64>) -> tensor<2x3x4x6xf32>

// CHECK:           %[[VAL_14:.*]] = "tf.Abs"(%[[VAL_1]]) : (tensor<2x3xi32>) -> tensor<2x3xi32>
// CHECK:           %[[VAL_15:.*]] = "tf.Div"(%[[VAL_13]], %[[VAL_14]]) : (tensor<2x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
// CHECK:           %[[VAL_16:.*]] = "tf.Select"(%[[VAL_6]], %[[VAL_7]], %[[VAL_15]]) : (tensor<2x3xi1>, tensor<2x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>

}

// -----

// Test invalid tf.Less
func.func @testLess(tensor<4xi32>, tensor<4xi32>) -> tensor<4xi32> {
^bb0(%arg0: tensor<4xi32>, %arg1: tensor<4xi32>):
  // expected-error @+1 {{op result #0 must be tensor of bool values}}
  %0 = "tf.Less"(%arg0, %arg1) : (tensor<4xi32>, tensor<4xi32>) -> tensor<4xi32>
  func.return %0 : tensor<4xi32>
}

// -----

// Test valid tf.ConcatV2

  %8 = "tfl.mul"(%2, %3) {fused_activation_function = "NONE"} : (tensor<4xi32>, tensor<4xi32>) -> tensor<4xi32>

  func.return %5, %6, %7, %8 : tensor<i32>, tensor<4xi32>, tensor<4xi32>, tensor<4xi32>
}

// CHECK-LABEL: @add_dense_splat_int
func.func @add_dense_splat_int() -> tensor<4xi32> {
  %0 = arith.constant dense<[-10, -1, 42, 100]> : tensor<4xi32>

    %1 = "tf.Const"() {value = dense<0> : tensor<1x1xi32>} : () -> tensor<1x1xi32>
    %2 = "tf.Const"() {value = dense<[7, 7, 3, 64]> : tensor<4xi32>} : () -> tensor<4xi32>
    %3 = "tf.Const"() {value = dense<[[0, 0], [3, 3], [3, 3], [0, 0]]> : tensor<4x2xi32>} : () -> tensor<4x2xi32>
    %4 = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>

  "tf_saved_model.global_tensor"() {is_mutable, sym_name = "y", type = tensor<3x1xi32>, value = dense<[[1], [2], [3]]> : tensor<3x1xi32>} : () -> ()
  "tf_saved_model.asset"() {sym_name = "z", filename = "file"} : () -> ()
  func.func @serving_default(
      %arg0: tensor<1x3xi32> {tf_saved_model.index_path = ["x"]},
      %arg1: tensor<!tf_type.resource<tensor<3x1xi32>>> {tf_saved_model.bound_input = @y},

@testLstmWithInvalidInputsRankMatch(%arg0: tensor<1x4xf32>, %arg1: tensor<4x2xf32>, %arg2: tensor<4x2xf32>, %arg3: tensor<4x2xf32>, %arg4: tensor<4x2xf32>, %arg5: tensor<4x4xf32>, %arg6: tensor<4x4xf32>, %arg7: tensor<4x4xf32>, %arg8: tensor<4x4xf32>, %arg9: tensor<4xf32>, %arg10: tensor<4xf32>, %arg11: tensor<4xf32>, %arg12: tensor<1x4xf32>, %arg13: tensor<4xf32>, %arg14: tensor<4xf32>, %arg15: tensor<4xf32>, %arg16: tensor<4x4xf32>, %arg17: tensor<4xf32>, %arg18: tensor<4xf32>, %arg19: tensor<4xf32>,...

// CHECK-DAG: %[[CONST:.*]] = "tf.Const"() <{value = dense<-131072> : tensor<1x3xi32>}> : () -> tensor<1x3xi32>
// CHECK: %[[MATMUL:.*]] = "tf.XlaDotV2"({{.*}}, %[[WEIGHT]])
// CHECK-SAME: (tensor<1x1024xi8>, tensor<1024x3xi8>) -> tensor<1x3xi32>
// CHECK: %[[SUB:.*]] = "tf.Sub"(%[[MATMUL]], %[[CONST]]) : (tensor<1x3xi32>, tensor<1x3xi32>) -> tensor<1x3xi32>
}

// -----

module attributes {} {

  %0:3 = "tf.SplitV"(%input, %split_sizes, %split_dim) : (tensor<4x6xf32>, tensor<3xi32>, tensor<i32>) -> (tensor<4x1xf32>, tensor<4x2xf32>, tensor<4x3xf32>)
  // CHECK: return %[[ONE]], %[[TWO]], %[[THREE]]
  func.return %0#0, %0#1, %0#2 : tensor<4x1xf32>, tensor<4x2xf32>, tensor<4x3xf32>
}

// -----

// -----

// Tests that int constants are not folded.

// CHECK-LABEL: transpose_int
func.func @transpose_int() -> tensor<3x2xi32> {
  %0 = stablehlo.constant dense<0> : tensor<2x3xi32>
  %1 = stablehlo.transpose %0, dims = [1, 0] : (tensor<2x3xi32>) -> tensor<3x2xi32>
  return %1 : tensor<3x2xi32>
}
// CHECK: transpose

// -----

// Tests that transposing an argument cannot be folded.

// CHECK-LABEL: transpose_arg