module attributes {tf.versions = {producer = 888 : i32}} {
func.func @testReshapeShapeInference(%arg0: tensor<3x4xi32>) -> tensor<*xi32> {
  %cst = arith.constant dense<[1, 6, 2]> : tensor<3xi32>
  // CHECK: "tfl.reshape"(%arg0, %cst) : (tensor<3x4xi32>, tensor<3xi32>) -> tensor<1x6x2xi32>
  %0 = "tfl.reshape"(%arg0, %cst) : (tensor<3x4xi32>, tensor<3xi32>) -> tensor<*xi32>
  func.return %0 : tensor<*xi32>
}
}

// -----

  // CHECK-NEXT: mhlo.xor
  %0 = "tf.BitwiseXor"(%arg0, %arg1) : (tensor<4xui32>, tensor<4xui32>) -> tensor<4xui32>
  func.return %0: tensor<4xui32>
}

// CHECK-LABEL: func @bitwise_and
func.func @bitwise_and(%arg0: tensor<4xi32>, %arg1: tensor<4xi32>) -> tensor<4xi32> {
  // CHECK-NEXT: mhlo.and
  %0 = "tf.BitwiseAnd"(%arg0, %arg1) : (tensor<4xi32>, tensor<4xi32>) -> tensor<4xi32>

  func.return
}

func.func private @write_chain_func(%arg0: tensor<1xf32>) -> (tensor<1xf32>) {
  %cst = "tf.Const"() <{value = dense<[[0, 1]]> : tensor<1x2xi32>}> : () -> tensor<1x2xi32>
  %0 = "tf.XlaAllReduce"(%arg0, %cst) <{mode = "CrossReplica", reduce_op = "Add"}> : (tensor<1xf32>, tensor<1x2xi32>) -> tensor<1xf32>
  return %0 : tensor<1xf32>

  %0 = mhlo.constant dense<[[1, 1], [2, 2]]> : tensor<2x2xi32>
  // NO_FALLBACK: tf.MirrorPad
  // SUPPORTED_FALLBACK_DEVICE-NOT: tf.MirrorPad
  // UNSPECIFIED_FALLBACK_DEVICE: tf.MirrorPad
  // UNSUPPORTED_FALLBACK_DEVICE: tf.MirrorPad
  %1 = "tf.MirrorPad"(%arg0, %0) {mode = "SYMMETRIC"} : (tensor<2x3xcomplex<f64>>, tensor<2x2xi32>) -> tensor<4x7xcomplex<f64>>
  func.return %1 : tensor<4x7xcomplex<f64>>
}

  %18 = "tf.Const"() {value = dense<1> : tensor<4xi32>} : () -> tensor<4xi32>
  %57 = "tf.Const"() {value = dense<0> : tensor<4xi32>} : () -> tensor<4xi32>
  %534 = "tf.StridedSlice"(%arg0, %57, %57, %18) {begin_mask = 11 : i64, device = "", ellipsis_mask = 0 : i64, end_mask = 11 : i64, new_axis_mask = 4 : i64, shrink_axis_mask = 0 : i64} : (tensor<*xf32>, tensor<4xi32>, tensor<4xi32>, tensor<4xi32>) -> tensor<*xf32>
  func.return %534 : tensor<*xf32>

func.func @my_cast(%arg0: tensor<2x3xf32>) -> tensor<2x3xi32> {
  %0 = "tf.MyCast"(%arg0) {Tout=i32} : (tensor<2x3xf32>) -> tensor<2x3xi32>
  func.return %0 : tensor<2x3xi32>

// CHECK-NEXT: %[[CAST:.*]] = "tf.RiscCast"(%arg0) {Tout = i32} : (tensor<2x3xf32>) -> tensor<*xi32>
// CHECK-NEXT: %[[ES:.*]] = "tf.EnsureShape"(%[[CAST]]) <{shape = #tf_type.shape<2x3>}> : (tensor<*xi32>) -> tensor<2x3xi32>
// CHECK-NEXT: return %[[ES]] : tensor<2x3xi32>
}

  } : (tensor<2x7xi64>) -> (tensor<2x7xi32>)
  %1 = "tf.Identity"(%0) : (tensor<2x7xi32>) -> tensor<2x7xi32>
  func.return %1 : tensor<2x7xi32>
}

// CHECK-LABEL: func @func
// CHECK-SAME: %arg0: tensor<2x7xi64> {mhlo.sharding = "\08\03\1A\03\01\01\02\22\02\00\01B\01\00"
func.func @func(%arg0: tensor<2x7xi64>) -> (tensor<2x7xi32>) {

  // CHECK-NEXT:     %[[GROUP:.*]] = "tf.Const"()
  // CHECK-SAME:       [0, 1, 2, 3]
  // CHECK-NEXT:     %[[REDUCED:.*]] = "tf.XlaAllReduce"(%[[ID]], %[[GROUP]]) <{mode = "CrossReplica", reduce_op = "Add"}> : (tensor<f32>, tensor<1x4xi32>) -> tensor<f32>
  // CHECK-NEXT:     "tf.OpA"(%[[REDUCED]]) : (tensor<f32>) -> ()
  tf_device.replicate {n = 4 : i32} {
    "tf_device.cluster"() ({
      "tf.OpA"(%arg0) : (tensor<f32>) -> ()
      tf_device.return

  %3 = "tf.AddV2"(%rhs_dilation, %rhs_dilation) {device = ""} : (tensor<3xi32>, tensor<3xi32>) -> tensor<3xi32>
  %4 = "tf.AddV2"(%padding, %padding) {device = ""} : (tensor<3x2xi32>, tensor<3x2xi32>) -> tensor<3x2xi32>
  %5 = "tf.AddV2"(%strides, %strides) {device = ""} : (tensor<3xi32>, tensor<3xi32>) -> tensor<3xi32>

  return %0 : tensor<8x4x14x14x16xf32>
}

  %3 = "tf.AddV2"(%arg0, %0): (tensor<4x4xf32>, tensor<1xf32>) -> tensor<4x4xf32>
  %4 = "tf.Log"(%3) {device = "/job:localhost/replica:0/task:0/device:GPU:0"}: (tensor<4x4xf32>) -> tensor<4x4xf32>

  // CHECK: %[[ADD1:.*]] = "tf.AddV2"
  // CHECK: %[[LOG1:.*]] = "tf.Log"(%[[ADD1]])
  %5 = "tf.AddV2"(%4, %1): (tensor<4x4xf32>, tensor<1xf32>) -> tensor<4x4xf32>