// CHECK-NOT: Optional
  %0 = "tf.OptionalFromValue"(%arg0) : (tensor<f32>) -> tensor<!tf_type.variant<tensor<f32>>>
  %1 = "tf.PartitionedCall"(%0) {
      config = "",
      config_proto = "",
      executor_type = "",
      f = @identity} : (tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant<tensor<f32>>>

    ```

    `_XlaMustCompile=true` in the following code

    ```mlir

  // `potential_refined_type`. Return true if the type was changed.
  bool RefineResultType(Operation* op, Value result,
                        Type potential_refined_type);

  // Infers the shape from a (Stateful)PartitionedCall operation by looking up
  // the called function and propagating the return type.
  bool InferShapeForCall(CallOpInterface call_op);

  bool InferShapeForCast(Operation* op);

    }
  }
  return false;
}

// Returns whether the current op is not supported by the TF Lite runtime.
static bool IsUnsupportedFlexOp(const std::string& op_name) {
  return op_name == "PartitionedCall" || op_name == "StatefulPartitionedCall";
}

// Create description of operation that could not be converted.
static std::string GetOpDescriptionForDebug(Operation* inst) {
  const int kLargeElementsAttr = 16;

  Value return_values[] = {selected_input, selected_index};
  b.create<ReturnOp>(return_values);
}

//===----------------------------------------------------------------------===//
// PartitionedCall op utilities.
//===----------------------------------------------------------------------===//

// Verify that the arguments to be passed into the function are the same types
// as the function paramter types.

  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
  TF_DerivedOperandTypeAttr dtype = TF_DerivedOperandTypeAttr<1>;
}

def TF_PartitionedCallOp : TF_Op<"PartitionedCall", [CallOpInterface, DeclareOpInterfaceMethods<SymbolUserOpInterface>, Pure]> {
  let summary = [{
returns `f(inputs)`, where `f`'s body is placed and partitioned.
  }];

  let description = [{