//  - gather_nd->transpose->gather_nd->transpose.
    if (!gather_nd_first->hasOneUse()) return failure();
    auto transpose_first =
        dyn_cast_or_null<TFL::TransposeOp>(*(gather_nd_first->user_begin()));
    if (!transpose_first || !transpose_first->hasOneUse()) return failure();
    auto gather_nd_second =
        dyn_cast_or_null<TFL::GatherNdOp>(*(transpose_first->user_begin()));

                                           fuse_bias_constant);
    }
    return output_type;
  }

  static bool HasOneUseByQuantizeOp(Operation* op) {
    return op->hasOneUse() &&
           (FindUserOfType<stablehlo::UniformQuantizeOp>(op) != nullptr ||
            FindUserOfType<TFL::QuantizeOp>(op) != nullptr);
  }
};

// Rewrites `stablehlo.convolution` into fused `tfl.conv_2d`.

    tensor_index_map_[subgraph_index][tensor_name] = tensors.size();
    std::optional<BufferOffset<tflite::QuantizationParameters>>
        quant_parameters;
    if (value.hasOneUse()) {
      auto stats_op =
          llvm::dyn_cast<mlir::quantfork::StatisticsOp>(*value.user_begin());
      if (stats_op) {
        quant_parameters = GetQuantizationForQuantStatsOpOutput(stats_op);
      }

  using OpRewritePattern<ReshapeOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(ReshapeOp reshape,
                                PatternRewriter& rewriter) const override {
    if (!reshape.getShape().hasOneUse()) return failure();

    DenseIntElementsAttr shape;
    if (!matchPattern(reshape.getShape(), m_Constant(&shape))) {
      return failure();
    }
    // It is already a 1-D constant, no change.