// CHECK:           return %[[VAL_2]] : tensor<10x8xi64>
// CHECK:         }
func.func @convert_remainder_for_int64(%arg0: tensor<10x8xi64>, %arg1: tensor<10x8xi64>) -> tensor<10x8xi64> {
  %0 = mhlo.remainder %arg0, %arg1 : tensor<10x8xi64>
  func.return %0 : tensor<10x8xi64>
}

// CHECK-LABEL:   func @not_convert_remainder_for_int16(
// CHECK-NOT:       "tf.Mod"

  %padding = "tf.Const"() { value = dense<[[1, 2]]> : tensor<1x2xi64> } : () -> tensor<1x2xi64>
  // CHECK: "mhlo.pad"(%arg0, %arg1) <{
  // CHECK-SAME: edge_padding_high = dense<2> : tensor<1xi64>,
  // CHECK-SAME: edge_padding_low = dense<1> : tensor<1xi64>,
  // CHECK-SAME: interior_padding = dense<0> : tensor<1xi64>
  %1 = "tf.PadV2"(%arg0, %padding, %arg1) : (tensor<3xf32>, tensor<1x2xi64>, tensor<f32>) -> tensor<6xf32>

  %1 = "tf.StringToHashBucketFast"(%arg0) {device = "", num_buckets = 2147483647 : i64} : (tensor<?x!tf_type.string>) -> tensor<?xi64>
  %2 = "tf.Sgnn"(%1, %0) {device = ""} : (tensor<?xi64>, tensor<10x1xi64>) -> tensor<10x?xf64>
  %3 = "tf.Const"() {value = dense<[-1, 10]> : tensor<2xi64>} : () -> tensor<2xi64>