// Tests input and output names from FlatBuffer are added to `tf.entry_function` attribute.

// CHECK-LABEL: @main
func.func @main(%arg0: tensor<4xi8>, %arg1: tensor<4xi32>) -> (tensor<4xi32>, tensor<4xi8>)
// CHECK: attributes {tf.entry_function = {inputs = "input0,input1", outputs = "output0,output1"}}
attributes {tf.entry_function = {inputs = "input0,input1", outputs = "output0,output1"}} {

          ],
          "name": "output0",
          "quantization": {
          }
        },
        {
          "shape": [
            128,
            32,
            32,
            3
          ],
          "name": "output1",
          "quantization": {
          }
        }
      ],
      "inputs": [
        0,
        1
      ],
      "outputs": [
        2,
        3
      ],

// RUN: tf-mlir-translate -mlir-to-graphdef %s -o - | FileCheck %s

func.func @main(%arg0: tensor<10xi32>) -> tensor<10xi32>
attributes {tf.entry_function = {inputs = "input0", outputs = "output0"}} {
  %graph = tf_executor.graph {
    tf_executor.fetch %arg0 : tensor<10xi32>
  }
  func.return %graph : tensor<10xi32>
}

// CHECK:      node {
// CHECK-NEXT:   name: "input0"
// CHECK-NEXT:   op: "_Arg"
// CHECK:          key: "T"

  %graph_result = tf_executor.graph {
    %output0, %control0 = tf_executor.island {
      %a = "tf.opA"(%arg0) : (tensor<i32>) -> tensor<i32>
      tf_executor.yield %a : tensor<i32>
    }
    %output1, %control1 = tf_executor.island {
      %b = "tf.opB"(%output0) : (tensor<i32>) -> tensor<i32>
      tf_executor.yield %b : tensor<i32>
    }
    tf_executor.fetch %output1 : tensor<i32>
  }

  func.func @main() {
// CHECK: arith.constant
// CHECK: TPUCompile
// CHECK: TPUExecute
// CHECK-NOT: func @_func
    tf_executor.graph {
      %outputs, %control = tf_executor.island wraps "arith.constant"() {value = dense<2.000000e+00> : tensor<f32>} : () -> tensor<f32>
      %outputs_0, %control_1 = tf_executor.island wraps "arith.constant"() {value = dense<3.000000e+00> : tensor<f32>} : () -> tensor<f32>

    %outputs_0, %control_0 = tf_executor.island wraps "tf.Placeholder"() {device = "", dtype = "tfdtype$DT_FLOAT", name = "y", shape = "tfshape$dim { }"} : () -> tensor<0xf32>
    %outputs_1, %control_1 = tf_executor.island wraps "tf.TPUReplicatedInput"(%outputs_0) {N = 1 : i64, T = "tfdtype$DT_FLOAT", device = "", name = "input1"} : (tensor<0xf32>) -> tensor<0xf32>

        }

        void createContents() {
            file.text = "output file"
            dir.file("some/dir/output1.txt") << "output1"
            dir.file("some/dir/output2.txt") << "output2"
            dir.file("some/output3.txt") << "output3"
            dir.file("some/another/output4.txt") << "output4"
            dir.createDir("some/lonelyDir")
        }
    }

	cmd.Flags().StringVarP(pf.OutputFormat, "output", "o", *pf.OutputFormat, fmt.Sprintf("Output format. One of: %s.", strings.Join(pf.AllowedFormats(), "|")))
	cmd.Flags().StringVarP(pf.OutputFormat, "experimental-output", "", *pf.OutputFormat, fmt.Sprintf("Output format. One of: %s.", strings.Join(pf.AllowedFormats(), "|")))
	_ = cmd.Flags().MarkDeprecated("experimental-output", "please use --output instead.")
}

Xiaopeng Han <******@****.***> 1686064488 +0800

        outputContains(output3)

        where:
        expression                                                                                      | output1                     | output2                            | output3