func.return %2, %3 : tensor<1x10xf32>, tensor<1x10xi64>
  }

  // CHECK: %[[RESOURCE:.*]] = "tfl.var_handle"() <{container = "c", shared_name = "a"}> : () -> tensor<!tf_type.resource<tensor<1x10xf32>>>
  // CHECK: %[[RESOURCE_1:.*]] = "tfl.var_handle"() <{container = "c", shared_name = "b"}> : () -> tensor<!tf_type.resource<tensor<1x10xi64>>>

  %group_assignment = "tf.Const"() { value = dense<[[0],[1]]> : tensor<2x1xi32> } : () -> tensor<2x1xi32>
  // CHECK: "mhlo.all_reduce"
  // CHECK{LITERAL}: replica_groups = dense<[[0], [1]]> : tensor<2x1xi64>
  // CHECK-NOT: channel_handle
  // CHECK: mhlo.add
  %0 = "tf.XlaAllReduce"(%input, %group_assignment) {reduce_op = "Add", mode = "CrossReplica"} : (tensor<f32>, tensor<2x1xi32>) -> tensor<f32>
  func.return %0 : tensor<f32>

  %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>

// CHECK-DAG: %[[cst_1:.*]] = "tf.Const"() <{value = dense<-1> : tensor<1xi64>}> : () -> tensor<1xi64>
// CHECK: %[[arg1_i64:.*]] = "tf.Cast"(%arg1) <{Truncate = false}> : (tensor<1xi32>) -> tensor<1xi64>
// CHECK: %[[tensor_scatter_update:.*]] = "tf.TensorScatterUpdate"(%[[cst]], %[[cst_0]], %[[arg1_i64]]) : (tensor<2xi64>, tensor<1x1xi64>, tensor<1xi64>) -> tensor<2xi64>

    %0 = stablehlo.constant dense<500> : tensor<100xi64>
    %1 = stablehlo.constant dense<0> : tensor<100xi64>
    %2 = stablehlo.compare  LT, %arg2, %1 : (tensor<100xi64>, tensor<100xi64>) -> tensor<100xi1>
    %3 = stablehlo.add %arg2, %0 : tensor<100xi64>
    %4 = stablehlo.select %2, %3, %arg2 : tensor<100xi1>, tensor<100xi64>
    %5 = stablehlo.reshape %4 : (tensor<100xi64>) -> tensor<100x1xi64>

// CHECK: %[[quant:.*]] = "tfr.cast"(%arg0) : (tensor<1x10xi8>) -> !tfr.tensor
// CHECK: %[[scale_cast:.*]] = "tfr.cast"(%[[scale]])
// CHECK: %[[zp_cast:.*]] = "tfr.cast"(%[[zp]])
// CHECK: %[[requant:.*]] = tfr.call @tf__requantize(%[[quant]], %[[scale_cast]], %[[zp_cast]])
// CHECK: %[[result:.*]] = "tfr.cast"(%[[requant]])
// CHECK-NOT: quant.uniform
// CHECK: return %[[result]], %[[float_resunt:.*]] : tensor<1x10xi8>, tensor<1x5xf32>
}

  func.func @xla_spmd_shard_to_full_shape(%arg0: tensor<1x2xi64>) -> (tensor<2x2xi64>) {
    // CHECK: %[[SHARDING:.*]] = mhlo.custom_call @Sharding(%arg0) {backend_config = "", mhlo.sharding = "{manual}"} : (tensor<1x2xi64>) -> tensor<1x2xi64>
    // CHECK: %[[FULL:.*]] = mhlo.custom_call @SPMDShardToFullShape(%[[SHARDING]]) {backend_config = "", mhlo.sharding = "{devices=[2,1]0,1}"} : (tensor<1x2xi64>) -> tensor<2x2xi64>
    // CHECK: return %[[FULL]]

  %padding = "tf.Const"() { value = dense<[[1, 2]]> : tensor<1x2xi64> } : () -> tensor<1x2xi64>
  // CHECK-DAG: [[PAD:%.+]] = "tf.Const"() {{.+}} -> tensor<1x2xi64>
  // CHECK-DAG: [[CST:%.+]] = "tf.Const"() <{value = dense<0.000000e+00> : tensor<f32>}>
  // CHECK: "tf.PadV2"(%arg0, [[PAD]], [[CST]])
  %0 = "tf.Pad"(%arg0, %padding) : (tensor<3xf32>, tensor<1x2xi64>) -> tensor<6xf32>
  func.return %0 : tensor<6xf32>
}

    %11 = "tf.Pad"(%arg0, %10) : (tensor<2x224x224x3xf32>, tensor<4x2xi32>) -> tensor<2x230x230x3xf32>
    %12 = "tf.Cast"(%arg1) {Truncate = false} : (tensor<2x1xf32>) -> tensor<2x1xi64>
    %13 = "tf.Reshape"(%12, %9) : (tensor<2x1xi64>, tensor<1xi32>) -> tensor<2xi64>
    %14 = "tf.Squeeze"(%arg1) {squeeze_dims = [-1]} : (tensor<2x1xf32>) -> tensor<2xf32>
    // CHECK: "tf.Conv2D"
    // CHECK-SAME: strides = [1, 1, 1, 1]

  %0 = "tf.Const"() {value = dense<1> : tensor<1x10xi32>} : () -> tensor<1x10xi32>
  %1 = "tf.Const"() {value = dense<1> : tensor<1xi32>} : () -> tensor<i32>
  func.return %0, %1 : tensor<1x10xi32>, tensor<i32>
}