REGISTER_OP("TestThreeInputsOp")
    .Input("x: T")
    .Input("y: T")
    .Input("z: T")
    .Output("output: T")
    .Attr("T: numbertype")
    .Attr("act: {'x', 'y', 'z'} = 'z'");

REGISTER_OP("TestTwoOutputsOp")
    .Input("input: T")
    .Output("output1: T")
    .Output("output2: T")
    .Attr("T: numbertype");

namespace tensorflow {
namespace experimental {
namespace cc {

// SignatureDefFunctionMetadata stores additional information on each input
// and output's names, dtypes, and shape.
class SignatureDefFunctionMetadata final {
  // TODO(bmzhao): Add getters here as necessary.
 private:
  friend class SignatureDefFunction;
  static SignatureDefFunctionMetadata* wrap(

apiVersion: release-notes/v2
kind: feature
area: istioctl
releaseNotes:
  - |

# RUN: not tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -tf-input-arrays=input -tf-input-data-types=DT_FLOAT -tf-input-shapes='' -tf-output-arrays=input:1 -o - 2>&1 | FileCheck %s

# CHECK: Invalid output index 1 specified for node: input

node {
  name: "input"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }

// WholeModel-DAG: "tf.DumpTensor"(%[[output1]]) <{enabled = true, file_name = "unquantized_tensor_data.pb", func_name = "conv", log_dir_path = "/tmp/dumps/composite_conv2d_with_bias_and_relu6_fn_1", node_name = "Conv2D_1"}> : (tensor<*xf32>) -> ()
// WholeModel-DAG: return %[[output0]], %[[output1]]

// IntPerLayer-LABEL: func @conv
// IntPerLayer-DAG: %[[w:.*]] = "tf.Const"() <{value = dense<{{\[\[\[\[}}1.600000e-01, 1.000000e-01

Paul Merlin <******@****.***> 1690890212 +0200

# RUN: not tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -tf-input-arrays=input -tf-input-data-types=DT_FLOAT -tf-input-shapes='' -tf-output-arrays=NotANodeInTheGraph -o - 2>&1 | FileCheck %s

# CHECK: Output NotANodeInTheGraph was not found in graph

node {
  name: "input"
  op: "Placeholder"
  device: "/device:CPU:0"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {

  const TF_TensorSpec* tensor_spec_out =
      TF_SignatureDefParamTensorSpec(param_out);
  const TF_Shape* shape_out = TF_TensorSpecShape(tensor_spec_out);

  // Output "output_0" is a scalar, float32 tensor
  EXPECT_EQ("output_0", std::string(TF_SignatureDefParamName(param_out)));
  EXPECT_EQ(TF_FLOAT, TF_TensorSpecDataType(tensor_spec_out));
  EXPECT_EQ(0, TF_ShapeDims(shape_out));

  input: "z:1"
  input: "z:2"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
}
versions {
  producer: 230
}

# Test non zero index output tensors as feeds. Original ops where their outputs
# are replaced with feeds are preserved and args and rets are lifted to the
# function. Rets that happen to coincide with a feed should have its value be
# of the feed.
#

// 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"