dim {
              size: 1
            }
          }
        }
      }
      method_name: "tensorflow/serving/regress"
    }
  }
  signature_def {
    key: "serving_default"
    value {
      inputs {
        key: "x"
        value {
          name: "x:0"
          dtype: DT_FLOAT
          tensor_shape {
            dim {
              size: -1

// CHECK-NEXT:  "tf_mlrt.tf_promise"(%arg1, [[ta_1]]) : (!mlrt.promise, tensor<!tf_type.variant<tensor<*xf32>>>) -> ()
// CHECK-NEXT: return

//CHECK-LABEL: @serving_default
func.func @serving_default(%arg0: tensor<?xf32> {tf.device = "/job:localhost/replica:0/task:0/device:CPU:0"}) -> tensor<3xf32> attributes {tf.entry_function = {control_outputs = "", inputs = "serving_default_input:0", outputs = "PartitionedCall:0"}} {

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string - | FileCheck %s

module {
func.func @add(%arg0: tensor<3x2xf32>) -> tensor<3x2xf32> attributes {tf.entry_function = {inputs = "serving_default_x", outputs = "outputs"}} {
  %0 = "tfl.pseudo_const" () {value = dense<[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]> : tensor<3x2xf32>} : () -> tensor<3x2xf32>

        value {
          name: "NoOp"
          tensor_shape {
            unknown_rank: true
          }
        }
      }
    }
  }
  signature_def {
    key: "serving_default"
    value {
      outputs {
        key: "output_0"
        value {
          name: "PartitionedCall:0"
          dtype: DT_DOUBLE
          tensor_shape {
            dim {

  versions:
  - name: v1
    served: true
    storage: true
    schema:
      openAPIV3Schema:
        type: object
        properties:
          string:
            type: string
          string_default:
            type: string
            default: ""
          string_null:
            type: string
            nullable: true

          boolean:
            type: boolean