pkg p3, func BadHop(int, int, int) (bool, bool, *ThirdBase, *ThirdBase, error)
pkg p3, method (*ThirdBase) GoodPlayer() (int, int, int)

// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

package p3

type ThirdBase struct{}

func (tb *ThirdBase) GoodPlayer() (i, j, k int)

struct TransposeCommuteWithPad : public OpRewritePattern<stablehlo::PadOp> {
  using OpRewritePattern<stablehlo::PadOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(stablehlo::PadOp pad_op,
                                PatternRewriter& rewriter) const override {
    Value pad_input = pad_op.getOperand();
    RankedTensorType pad_type = pad_op.getType().cast<RankedTensorType>();

  while (pad_op || cast_op) {
    if (pad_op) {
      auto block_arg_num = GetBlockArgNum(pad_op.getInput());
      if (block_arg_num.has_value()) return block_arg_num;
      next_input = pad_op.getInput();
    } else {
      auto block_arg_num = GetBlockArgNum(cast_op.getX());
      if (block_arg_num.has_value()) return block_arg_num;
      next_input = cast_op.getX();
    }

        stb_op, "result for current op has more than 1 use");
  }

  // Pad op.
  TF::PadOp pad_op;
  ElementsAttr pad_attr;
  if (llvm::isa<TF::PadOp>(consumer_op)) {
    pad_op = llvm::cast<TF::PadOp>(consumer_op);
    if (!pad_op.getResult().hasOneUse()) {
      return rewriter.notifyMatchFailure(
          pad_op, "result for current op has more than 1 use");
    }

LogicalResult MergeConsecutivePad(mhlo::PadOp pad_op,
                                  PatternRewriter &rewriter) {
  // Fail for non-static shapes
  if (!pad_op.getOperand().getType().hasStaticShape() ||
      !pad_op.getResult().getType().hasStaticShape() ||
      !pad_op.getPaddingValue().getType().hasStaticShape()) {
    return rewriter.notifyMatchFailure(pad_op, "dynamic shapes not supported");
  }

      // Add an extra tfl.pad_op if there are explicit padding values. This
      // extra pad op will allow us to always set the `padding` attribute of
      // the newly created tfl.conv_2d op as "VALID".
      TFL::PadOp pad_op =
          CreateTflPadOp(op.getLoc(), padding_attr, input_value, rewriter);
      input_value = pad_op.getResult();
    }

- [https://hadoop.apache.org/docs/current/hadoop-aws/tools/hadoop-aws/index.html](https://hadoop.apache.org/docs/current/hadoop-aws/tools/hadoop-aws/index.html)
- [https://hadoop.apache.org/docs/r3.1.1/hadoop-aws/tools/hadoop-aws/committers.html](https://hadoop.apache.org/docs/r3.1.1/hadoop-aws/tools/hadoop-aws/committers.html)

Once the config changes are applied, proceed to restart **Hadoop** services.

TAC_REGISTER_GPU_OP(MaximumOp, CreateBasicOpNoCost);
TAC_REGISTER_GPU_OP(MinimumOp, CreateBasicOpNoCost);
TAC_REGISTER_GPU_OP(MeanOp, CreateBasicOpNoCost);
TAC_REGISTER_GPU_OP(CustomOp, CreateBasicOpNoCost);
TAC_REGISTER_GPU_OP(PadOp, CreateBasicOpNoCost);
TAC_REGISTER_GPU_OP(PowOp, CreateBasicOpNoCost);
TAC_REGISTER_GPU_OP(PReluOp, CreateBasicOpNoCost);
TAC_REGISTER_GPU_OP(ReluOp, CreateBasicOpNoCost);
TAC_REGISTER_GPU_OP(Relu6Op, CreateBasicOpNoCost);

  llvm::SmallVector<int64_t, 8> slice_begins;
  llvm::SmallVector<int64_t, 8> slice_sizes;

  for (auto p : llvm::zip(pad_op.getOperand().getType().getShape(),
                          pad_op.getEdgePaddingLow().getValues<APInt>(),
                          pad_op.getEdgePaddingHigh().getValues<APInt>())) {
    const int64_t input_dim_size = std::get<0>(p);
    int64_t pad_output_dim_size = input_dim_size;