let summary = "Bidirectional sequence lstm operator";

  let description = [{
    Bidirectional lstm is essentially two lstms, one running forward & the
    other running backward. And the output is the concatenation of the two
    lstms.
  }];

  let arguments = (
    ins TFL_TensorOf<[F32, I8]>:$input,

    // Forward LSTM Weights

  // expected-error @+1 {{'tfl.lstm' op failed to verify that either projection weight must be specified or both projection weight and projection bias must not be specified}}

// CHECK-DAG:       [[VAL_47:%.*]] = arith.constant dense<0.000000e+00> : tensor<1x3xf32>
// CHECK-DAG:       [[VAL_48:%.*]] = arith.constant dense<0.000000e+00> : tensor<1x1xf32>

  // duplicated buffer is found.
  std::optional<BufferOffset<tflite::Buffer>> BuildBuffer(
      Value value, bool can_be_deduplicated, int& index);

  // Build TFLite tensor from the given type. This function is for tfl.lstm
  // intermediates, which should have UniformQuantizedType.
  std::optional<BufferOffset<tflite::Tensor>> BuildTensorFromType(
      mlir::Type type, const std::string& name);

    if (is_layer_norm_lstm) {
      const auto forget_layer_norm_coefficients =
          op.getForgetLayerNormCoefficients().getType().cast<ShapedType>();
      // If this lstm has layer normalization, this input value,
      // "forget_layer_norm_coefficients" should be a 1D tensor.
      if (!forget_layer_norm_coefficients.hasRank() ||
          forget_layer_norm_coefficients.getRank() != 1 ||