%2 = "stablehlo.concatenate"(%0, %1) {dimension = 0 : i64} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
  %3 = "quantfork.stats"(%2) {layerStats = dense<[-0.83811146, 2.4960899]> : tensor<2xf32>} : (tensor<?xf32>) -> tensor<?xf32>
  %4 = "quantfork.stats"(%arg2) {layerStats = dense<[-1.5726943, 1.07351148]> : tensor<2xf32>} : (tensor<?xf32>) -> tensor<?xf32>

  func.return %1, %2 : tensor<*xf32>, tensor<*xf32>

// CHECK:  %0 = "tf.Gather"(%arg0, %arg2) <{validate_indices = true}> : (tensor<*xf32>, tensor<i32>) -> tensor<*xf32>
// CHECK: return %0, %arg0 : tensor<*xf32>, tensor<*xf32>
}

// -----

// CHECK-LABEL: tensorlistStackWithConstantElementShape
func.func @tensorlistStackWithConstantElementShape(%arg0: tensor<?x3xf32>) -> (tensor<2x3xf32>) {

      %a, %control1 = tf_executor.island wraps "tf.A"() {_class = ["loc:@class"]} : () -> (tensor<2xf32>)
      %s:2, %control2 = tf_executor.island wraps "tf.Split"(%c, %a) {num_split = 2 : i32} : (tensor<i32>, tensor<2xf32>) -> (tensor<1xf32>, tensor<1xf32>)
      tf_executor.fetch
    }
    func.return
  }

    "tf.AssignVariableOp"(%2, %arg0) : (tensor<!tf_type.resource<tensor<?xf32>>>, tensor<?xf32>) -> ()
    return
  }
}

// -----

// CHECK-LABEL: module
module attributes {tf_saved_model.semantics} {
  // CHECK: ml_program.global{{.*}}[[V:@.+]]({{.*}}) : tensor<3xf32>
  // CHECK: func.func @read_inserts_cast_if_necessary(%arg0: tensor<?xf32>
  // CHECK: [[V1:%.*]] = ml_program.global_load

        tensor<2xf32>, tensor<2xf32>, tensor<2xf32>,
        tensor<2xf32>, tensor<2xf32>, tensor<2xf32>, tensor<2xf32>,
        tensor<4x2xf32>, tensor<4xf32>,
        tensor<1x4xf32>, tensor<1x2xf32>,
        tensor<2xf32>, tensor<2xf32>, tensor<2xf32>, tensor<2xf32>) -> tensor<*xf32>
  %24 = "quantfork.stats"(%23) {layerStats = dense<[-1.0, 2.0]> : tensor<2xf32>} : (tensor<*xf32>) -> tensor<*xf32>

  %4 = "tfr.cast"(%concat) : (!tfr.tensor) -> tensor<3xf32>
  func.return %4 : tensor<3xf32>

// CHECK: %[[concat:.*]] = "tf.RiscConcat"(%arg0, %arg1, %arg2) : (tensor<f32>, tensor<f32>, tensor<f32>) -> tensor<*xf32>
// CHECK: %[[es:.*]] = "tf.EnsureShape"(%[[concat]]) <{shape = #tf_type.shape<3>}> : (tensor<*xf32>) -> tensor<3xf32>
// CHECK: return %[[es]] : tensor<3xf32>
}

// CHECK-LABEL: decompose_tf_split

  # Note that semantically in Python, multiple return values are equivalent
  # to returning a tuple/list.
  #
  # CHECK:      func {{@[a-zA-Z_0-9]+}}() -> (
  # CHECK-SAME:   tensor<1xf32> {tf_saved_model.index_path = [0]},
  # CHECK-SAME:   tensor<2xf32> {tf_saved_model.index_path = [1]})
  # CHECK-SAME: attributes {{.*}} tf_saved_model.exported_names = ["f0001_multiple_results_no_punctuation"]
  @tf.function(input_signature=[])

"TANH", input_to_cell_intermediate = tensor<0xf32>, input_to_forget_intermediate = tensor<0xf32>, input_to_input_intermediate = tensor<0xf32>, input_to_output_intermediate = tensor<0xf32>, proj_clip = 0.000000e+00 : f32, time_major = false} : (tensor<?x?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>,...

func.func @multiply(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {
  %0 = "vhlo.multiply_v1"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  func.return %0 : tensor<1xf32>
}

// CHECK:func.func private @multiply(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {
// CHECK: %0 = "vhlo.multiply_v1"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
// CHECK: return %0 : tensor<1xf32>
// CHECK:}

// CHECK-LABEL: func @maximum
func.func @maximum(%arg0: tensor<4xf32>, %arg1: tensor<4xf32>) -> tensor<4xf32> {
  // CHECK-NEXT:  mhlo.maximum %arg0, %arg1 : tensor<4xf32>
  %0 = "tf.Maximum"(%arg0, %arg1) : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
  func.return %0 : tensor<4xf32>
}

// CHECK-LABEL: func @minimum
func.func @minimum(%arg0: tensor<4xf32>, %arg1: tensor<4xf32>) -> tensor<4xf32> {
  // CHECK-NEXT:  mhlo.minimum %arg0, %arg1 : tensor<4xf32>