// CHECK-DAG: %[[input_23:.*]] = "tfl.dequantize"({{.*}}) : (tensor<2x!quant.uniform<i16<-32767:32767>:f32, 2.736719606284107E-5>>) -> tensor<2xf32>

// CHECK: %[[lstm:.*]] = "tfl.lstm"(%[[input_0]], %[[none]], %[[input_2]], %[[input_3]], %[[input_4]], %[[none]], %[[input_6]], %[[input_7]], %[[input_8]],

                 op_or_err.status();
        }
        vals_map[input_num] = op_or_err.value()->getResult(0);
      }
    }

    // Intermediate tensors for LSTMs are used to carry quantization range
    // in their types, so we only need and extract their types.
    std::vector<mlir::TensorType> intermediate_types;
    intermediate_types.reserve(5);

  target.addDynamicallyLegalOp<TF::YieldOp>(is_legal);
  target.addDynamicallyLegalOp<TF::TensorListSetItemOp>(is_set_item_legal);
  target.addLegalOp<TFL::CustomOp>();
  // Register fused LSTM/RNN ops as legal.
  target.addLegalOp<TFL::LSTMOp>();
  target.addLegalOp<TFL::UnidirectionalSequenceLSTMOp>();
  target.addLegalOp<TFL::UnidirectionalSequenceRNNOp>();