/*projection_bias=*/none,
      /*input_activation_state=*/output_init_state,
      /*input_cell_state=*/hidden_init_state,
      /*input_layer_norm_coefficients=*/none,
      /*forget_layer_norm_coefficients=*/none,
      /*cell_layer_norm_coefficients=*/none,
      /*output_layer_norm_coefficients=*/none,
      /*fused_activation_function*/ builder->getStringAttr("TANH"),

    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 ||

    TFL_StatefulTensor:$input_cell_state,

    // Layer norm coefficients
    TFL_TensorOfOrNone<[F32, QI16]>:$input_layer_norm_coefficients,
    TFL_TensorOfOrNone<[F32, QI16]>:$forget_layer_norm_coefficients,
    TFL_TensorOfOrNone<[F32, QI16]>:$cell_layer_norm_coefficients,
    TFL_TensorOfOrNone<[F32, QI16]>:$output_layer_norm_coefficients,

    // Attributes
    TFL_AFAttr:$fused_activation_function,

  Value proj_bias_;

  // state
  Value input_activation_state_;
  Value input_cell_state_;

  // layer norm coefficients
  Value input_layer_norm_coefficients_;
  Value forget_layer_norm_coefficients_;
  Value cell_layer_norm_coefficients_;
  Value output_layer_norm_coefficients_;

  mlir::TFL::LSTMOp lstm_;

  Value none_;
  SmallVector<int64_t, 1> bias_slice_shape_;