func.return %0 : tensor<*xf32>
}

func.func @xla_func_0(%arg0: tensor<7xf32>, %arg1: tensor<f32>) -> tensor<10xf32> {
  %cst = "tf.Const"() {value = dense<0> : tensor<1x2xi32>} : () -> tensor<1x2xi32>
  %cst_0 = "tf.Const"() {value = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>
  %cst_1 = "tf.Const"() {value = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>

  %1 = "tfl.bucketize"(%0) {boundaries = [0.0 : f32, 10.0 : f32, 100.0 : f32]} : (tensor<3x2xf32>) -> tensor<3x2xi32> loc("bucketize")
  func.return %1 : tensor<3x2xi32>

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>
}
}

// -----

// fixed upstream).
func.func @broadcast_add(%arg0: tensor<1xi32>, %arg1: tensor<1x2xi32>) -> tensor<1x2xi32> {
  // CHECK-NEXT: %[[LHS_BCAST:.+]] = "mhlo.broadcast_in_dim"(%arg0) <{broadcast_dimensions = dense<1> : tensor<1xi64>}>
  // CHECK-NEXT: mhlo.add %[[LHS_BCAST]], %arg1
  %0 = "tf.AddV2"(%arg0, %arg1) : (tensor<1xi32>, tensor<1x2xi32>) -> tensor<1x2xi32>
  func.return %0: tensor<1x2xi32>
}

// CHECK-LABEL: func @broadcast_multi_dim_add

  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>>
}

// -----

func.func @testConcat(%arg0: tensor<1x2xi32>, %arg1: tensor<1x2xi32>) -> tensor<2x2xi32> {
  // CHECK: "tfl.concatenation"(%arg0, %arg1) <{axis = 0 : i32, fused_activation_function = "NONE"}>
  %0 = "tfl.concatenation"(%arg0, %arg1) {axis = 0 : i32, fused_activation_function = "NONE"} : (tensor<1x2xi32>, tensor<1x2xi32>) -> tensor<2x2xi32>
  func.return %0 : tensor<2x2xi32>
}

// -----

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>
}

  %0 = "tfl.pseudo_qconst"() { qtype = tensor<3x3x!quant.uniform<i32:f32:1, {1.0, 0.5:1, 0.25:1}>>, value = dense<1> : tensor<3x3xi32>} : () -> tensor<3x3x!quant.uniform<i32:f32:1, {1.0, 0.5:1, 0.25:1}>>

// CHECK-NEXT:  %[[argmax:.*]] = "tfl.arg_max"(%[[softmax]], %[[cst]]) : (tensor<128x16xf32>, tensor<i32>) -> tensor<128xi32>
  %3 = "tfl.arg_max"(%2, %cst) : (tensor<128x16xf32>, tensor<i32>) -> tensor<128xi32>
// CHECK-NEXT:  return %[[softmax]], %[[argmax]] : tensor<128x16xf32>, tensor<128xi32>
  func.return %2, %3 : tensor<128x16xf32>, tensor<128xi32>
}

// CHECK-LABEL: PruneUnusedLstm