absl::Span<tensorflow::AbstractTensorHandle* const> inputs,
    absl::Span<tensorflow::AbstractTensorHandle*> outputs) {
  // TODO(srbs): Provide a function execution API on ctx so that we do not
  // expose the internals of how functions are to be executed here.
  std::string fname;
  {
    const tensorflow::FunctionDef* fdef = nullptr;
    TF_RETURN_IF_ERROR(trace->GetFunctionDef(&fdef));

// CHECK-NEXT:       input_arg {
// CHECK-NEXT:         name: "function0"
// CHECK:            input_arg {
// CHECK-NEXT:         name: "function01"
// CHECK:            output_arg {
// CHECK-NEXT:         name: "function02"
// CHECK:          node_def {
// CHECK-NEXT:       name: "[[NAME:[^"]*]]"
// CHECK-NEXT:       op: "Identity"
// CHECK-NEXT:       input: "function0"
// CHECK:          ret {

  // to proactively transfer the result to host since the consumer op (or
  // function) of the result may still be on TPU.
  // TODO(b/194081364): remove this option once we unify servo TPU serving
  // result transfer behavior.
  bool tpu_transfer_result_to_host = false;
};

// Compile MLIR generated by tf.function in TF dialect into BEF.
Status CompileTFMLIRToBEF(const TfrtFunctionCompileOptions& options,

#include "tensorflow/core/platform/statusor.h"

namespace tf {
namespace libtf {

class Function {
 public:
  tensorflow::Status RegisterTrace(tensorflow::AbstractFunctionPtr,
                                   TaggedValue input_signature,
                                   TaggedValue output_signature);

  // Executes this function under the execution context.
  //
  // Raises an error is no matching signature is found for TaggedValue.

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include "tensorflow/compiler/mlir/tfrt/function/function.h"

#include "absl/log/log.h"
#include "absl/strings/match.h"
#include "mlir/IR/OperationSupport.h"  // from @llvm-project
#include "mlir/Pass/PassManager.h"  // from @llvm-project

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -tf-graph-as-function -tf-xla-compile-device-type="GPU" -tf-enable-soft-placement-on-import=true -o - | FileCheck %s

# Verify main graph was converted to a function, args/rets are mapped correctly,
# and ops in the main graph are retained. In addition, check if subsequent
# functions are converted.

    signature {
      name: "foo3"
    }
  }
  function {
    signature {
      name: "foo4"
    }
  }
  function {
    signature {
      name: "foo5"
    }
  }
  function {
    signature {
      name: "foo6"
    }
  }
  function {
    signature {
      name: "foo7"
    }
  }
  function {
    signature {
      name: "foo8"
    }
  }
  function {
    signature {

    tf_executor.fetch
  }
  func.return
}

// CHECK:      library {
// CHECK:        function {
// CHECK-NEXT:     signature {
// CHECK-NEXT:       name: "bar"
// CHECK-NEXT:     }
// CHECK:          node_def {
// CHECK-NEXT:       name: "tf.Const"
// CHECK-NEXT:       op: "Const"
// CHECK-NEXT:       attr {

Ilya Kirillov <******@****.***> 1665754261 +0200

}
node {
  name: "custom_embedding_matmul_func_call"
  op: "custom_embedding_matmul"
}
library {
  function {
    signature {
      name: "custom_relu"
    }
    attr {
      key: "_implements"
      value {
        func {
          name: "tensorflow.relu"
        }
      }
    }
  }
  function {
    signature {
      name: "custom_embedding_matmul"
    }
    attr {
      key: "_implements"