}
      experimental_debug_info {
        original_node_names: "const_2"
      }
    }
    ret {
      key: "bar"
      value: "const_2:output:0"
    }
  }
  function {
    signature {
      name: "foo"
      output_arg {
        name: "foo"
        type: DT_FLOAT
      }
    }
    node_def {
      name: "const_1"
      op: "Const"
      attr {
        key: "dtype"

}
// CHECK: func.func @main(%[[ARG:.+]]: tensor<1x3x4x3xf32>) -> tensor<1x3x2x2xf32> {
// CHECK-DAG: %[[CONST_0:.*]] = stablehlo.constant dense<[{{.*}}]> : tensor<2xf32>
// CHECK-DAG: %[[CONST_1:.*]] = stablehlo.constant dense<6.000000e+00> : tensor<f32>
// CHECK-DAG: %[[CONST_2:.*]] = stablehlo.constant dense<0.000000e+00> : tensor<f32>
// CHECK-DAG: %[[CONST_3:.*]] = stablehlo.constant dense<[{{.*}}]> : tensor<2x3x3x2xf32>

// CHECK-DAG: %[[CONST_0:.*]] = "tf.Const"() <{value = dense<{{.*}}> : tensor<1x1x3x2xf32>}> : () -> tensor<1x1x3x2xf32>
// CHECK-DAG: %[[CONST_1:.*]] = "tf.Const"() <{value = dense<-1.000000e+00> : tensor<f32>}> : () -> tensor<f32>
// CHECK-DAG: %[[CONST_2:.*]] = "tf.Const"() <{value = dense<1.000000e+00> : tensor<f32>}> : () -> tensor<f32>

      }
    }
  }
}
node {
  name: "Const_1"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_STRING
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_STRING
        tensor_shape {
        }
        string_val: "value"
      }
    }
  }
}
node {
  name: "Const_2"
  op: "Const"
  attr {
    key: "dtype"
    value {

// CHECK-DAG: %[[CONST_2:.+]] = stablehlo.constant dense<3> : tensor<1xi32>
// CHECK-DAG: %[[CONST_3:.+]] = stablehlo.constant dense<4> : tensor<1xi32>
// CHECK-DAG: %[[CONST_4:.+]] = stablehlo.constant dense<2> : tensor<1xi32>

// CHECK-DAG: %[[CONST_8:.*]] = "tf.Const"() <{value = dense<[162, 160]> : tensor<2xi32>}> : () -> tensor<2xi32>
// CHECK: %[[PADV2_0:.*]] = "tf.PadV2"({{.*}}, %[[CONST_4]], %[[CONST_5]]) : (tensor<1x3x4x3xi8>, tensor<4x2xi32>, tensor<i8>) -> tensor<1x4x5x3xi8>
// CHECK: %[[XLACONVV2_0:.*]] = "tf.XlaConvV2"(%[[PADV2_0]], %[[CONST_6]], %[[CONST_0]], %[[CONST_3]], %[[CONST_1]], %[[CONST_1]], %[[CONST_2]])

    tf_executor.fetch %1#0 : tensor<10xf32>
  }
  func.return %0 : tensor<10xf32>
}

// CHECK-DAG: name: "bar"
func.func @bar() -> tensor<10xf32> {
  %0 = tf_executor.graph {
    %1:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_FLOAT", value = dense<2.000000e+00> : tensor<10xf32>} : () -> tensor<10xf32> loc("const_2")
    tf_executor.fetch %1#0 : tensor<10xf32>

  %3 = stablehlo.add %2, %1 : tensor<1x3xf32>
  func.return %3: tensor<1x3xf32>
}
// CHECK: %[[CONST_0:.*]] = stablehlo.constant dense<2.000000e+00>
// CHECK: %[[CONST_1:.*]] = stablehlo.constant dense<2.000000e+00>
// CHECK: %[[XLA_CALL_MODULE:.*]] = "tf.XlaCallModule"(%arg0, %[[CONST_0]], %[[CONST_1]])
// CHECK: return %[[XLA_CALL_MODULE:.*]] : tensor<1x3xf32>
// CHECK: }

  }

// PerTensor-LABEL: func @depthwise_conv
// PerTensor-DAG: %[[CONST_0:.*]] = arith.constant dense<0.000000e+00> : tensor<6xf32>
// PerTensor: %[[CONST_1:.*]] = arith.constant dense
// PerTensor-NOT: tensor<2x3x1x6xf32>
// PerTensor-SAME: tensor<2x3x3x2xf32>

  func.return %2, %4 : tensor<*xf32>, tensor<*xf32>

// CHECK-DAG: %[[CONST_0:.*]] = "tf.Const"() <{value = dense<0.000000e+00> : tensor<2xf32>}> : () -> tensor<2xf32>
// CHECK-DAG: %[[CONST_1:.*]] = "tf.Const"() <{value = dense<3.000000e+00> : tensor<2x3x3x2xf32>}> : () -> tensor<2x3x3x2xf32>
// CHECK: %[[PARTITIONEDCALL_0:.*]] = "tf.PartitionedCall"(%arg0, %[[CONST_1]])
// CHECK-SAME: f = @composite_conv2d_fn_2}>