%0 = "tfl.conv_2d"(%arg0, %arg1, %cst_0) {dilation_h_factor = 2 : i32, dilation_w_factor = 3 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 4 : i32, stride_w = 5 : i32} : (tensor<256x32x32x3xbf16>, tensor<16x3x3x3xbf16>, tensor<16xbf16>) -> tensor<256x8x7x16xbf16>

  %0 = "tf.Const"() {value = dense<[1, 2, 3, 4]> : tensor<4xui64>} : () -> tensor<4xui64>
  // CHECK: tfrt_fallback_async.const_dense_tensor  dense<1.000000e+00> : tensor<1xbf16>
  %1 = "tf.Const"() {device = "/device:CPU:0", value = dense<[1.0]> : tensor<1xbf16>} : () -> tensor<4xbf16>
  // CHECK: corert.executeop({{.*}}) "tf.Const"() {dtype = ui64, value = dense<[1, 2, 3, 4]> : tensor<4xui64>} : 1

func.func @cast_bf16_add_v2_to_fp32(%arg0: tensor<2xbf16>, %arg1: tensor<2xbf16>) -> tensor<2xf32> {
  %0 = "tf.AddV2"(%arg0, %arg1) : (tensor<2xbf16>, tensor<2xbf16>) -> tensor<2xbf16>
  %1 = "tf.Cast"(%0) {Truncate = false} : (tensor<2xbf16>) -> tensor<2xf32>
  return %1 : tensor<2xf32>
}
// The signature of the function is not changed.
// CHECK: func @cast_bf16_add_v2_to_fp32(%[[ARG_0:.*]]: tensor<2xbf16>, %[[ARG_1:.*]]: tensor<2xbf16>) -> tensor<2xf32>

favicon-16x16.png...

    // CHECK: %[[CAST_1:.*]] = "tf.Cast"(%[[ARG_1]]) <{Truncate = false}> : (tensor<10xf32>) -> tensor<10xbf16>
    // CHECK: %[[RESULT:.*]]:2 = "tf.XlaCallModule"(%[[CAST_0]], %[[CAST_1]], %[[ARG_2]])
    // CHECK-SAME: (tensor<10xbf16>, tensor<10xbf16>, tensor<6xi32>) -> (tensor<10xbf16>, tensor<6xi32>)

// RUN: tf-opt %s -tfl-prepare-tf -tfl-legalize-tf='run-tfl-runtime-verification=false'  -tfl-optimize | FileCheck %s

func.func @broadcast_to_bf16(%arg0: tensor<3xbf16>, %arg1: tensor<2xi64>) -> tensor<3x3xbf16> {
  %0 = "tf.BroadcastTo"(%arg0, %arg1) : (tensor<3xbf16>, tensor<2xi64>) -> tensor<3x3xbf16>
  func.return %0: tensor<3x3xbf16>

// CHECK-LABEL: broadcast_to_bf16
// CHECK:  [[CST:%.*]] = arith.constant dense<1.000000e+00> : tensor<3x3xbf16>

    return %1: tensor<1x3xbf16>
  }

  %cst_1 = arith.constant dense<[1, -1, 6, 1, 6, 1, 1, -1]> : tensor<8xi64>
  %97 = "tf.BroadcastTo"(%arg0, %cst_1) : (tensor<1x?x1x1x1x1x1x?xbf16>, tensor<8xi64>) -> tensor<1x?x6x1x6x1x1x?xbf16>
  %cst_2 = arith.constant dense<[8, 6, 6, 18]> : tensor<4xi64>
  %98 = "tf.Reshape"(%97, %cst_2) : (tensor<1x?x6x1x6x1x1x?xbf16>, tensor<4xi64>) -> tensor<8x6x6x18xbf16>

}

func.func private @testIf3Then(tensor<*xf32>) -> (tensor<*xf32>, tensor<*xi8>, tensor<*xbf16>)
func.func private @testIf3Else(tensor<*xf32>) -> (tensor<*xf32>, tensor<*xi8>, tensor<*xbf16>)

// CHECK-LABEL: func @testIf3Result(%arg0: tensor<i1>, %arg1: tensor<*xf32>)
func.func @testIf3Result(tensor<i1>, tensor<*xf32>) -> (tensor<*xf32>, tensor<*xi8>, tensor<*xbf16>) {
^bb0(%arg0: tensor<i1>, %arg1: tensor<*xf32>):
  %1:3 = "tf.If"(%arg0, %arg1) {

      return %0 : tensor<1x?x4xbf16>
    }
  })";

  TF_ASSERT_OK(LegalizeModule(kModuleWithNonConstParam));
}

TEST_F(Tf2XlaRewriterTest, ErrorsWithInvalidNumberOfParametersToArgs) {