[](const Scope& s, const std::vector<Output>& inputs, Output* output) {
        *output = ops::Less(s, inputs[0], 10);
        return s.status();
      },
      [](const Scope& s, const std::vector<Output>& inputs,
         std::vector<Output>* outputs) {
        outputs->push_back(ops::AddN(s, {inputs[0], inputs[1]}));
        outputs->push_back(ops::AddN(s, {inputs[1], 1}));
        outputs->push_back(inputs[2]);

  }

  func.func @mul1(%arg0: tensor<1xf32> {tf_saved_model.index_path = ["y"]}, %arg1: tensor<1xf32> {tf_saved_model.index_path = ["x"]}) -> (tensor<1xf32> {tf_saved_model.index_path = ["output_0"]}) attributes {tf.entry_function = {inputs = "mul1_y:0,mul1_x:0", outputs = "output:0"}, tf_saved_model.exported_names = ["mul1"]} {
    %0 = "tf.Mul"(%arg1, %arg0) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
    func.return %0 : tensor<1xf32>
  }

      type: DT_FLOAT
    }
  }
}
node {
  name: "output_0"
  op: "Identity"
  input: "conv_net_2d_1/Relu"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
}
library {
}

# CHECK: 'main' inputs:
# CHECK-NEXT: name: 'input'
# CHECK-NEXT: 'main' outputs:

  ResetOutputValues();
  outputs_.clear();
  for (TF_Operation* o : outputs) {
    outputs_.emplace_back(TF_Output{o, 0});
  }
  output_values_.resize(outputs_.size());
}

void CSession::SetOutputs(const std::vector<TF_Output>& outputs) {
  ResetOutputValues();
  outputs_ = outputs;
  output_values_.resize(outputs_.size());
}

    %0 = tf_executor.graph {
      %outputs_0, %control_0 = tf_executor.island wraps "tf.VarHandleOp"() {container = "", shared_name = ""} : () -> tensor<!tf_type.resource<tensor<501000x128xf32>>>
      %outputs_1, %control_1 = tf_executor.island wraps "tf.Cast"(%outputs_0) {Truncate = false} : (tensor<!tf_type.resource<tensor<501000x128xf32>>>) -> tensor<*x!tf_type.resource>

    value {
      s: "cluster"
    }
  }
}
node {
  name: "output0"
  op: "TPUReplicatedOutput"
  input: "Identity"
  attr {
    key: "T"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "num_replicas"
    value {
      i: 1
    }
  }
}
node {
  name: "output1"
  op: "TPUReplicatedOutput"
  input: "Identity_1"
  attr {
    key: "T"

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-unconditionally-use-set-output-shapes-on-import -tf-enable-shape-inference-on-import=false -tf-graph-as-function %s -o -  | FileCheck %s
# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-unconditionally-use-set-output-shapes-on-import -tf-enable-shape-inference-on-import=true -tf-graph-as-function %s -o - | FileCheck %s

# Verify importing with _output_shapes enabled works as expected.

node {
  name: "_Arg"

                @Override
                void transform(TransformOutputs outputs) {
                    def input = inputArtifact.get().asFile
                    def output1 = outputs.file(input.name + ".1." + target)
                    def output2 = outputs.file(input.name + ".2." + target)
                    if (showOutput) {

Benjamin Elder <******@****.***> 1547627953 -0800

Laura Kassovic <******@****.***> 1701107622 -0800