SideEffects:    &noSideEffect,
			TimeoutSeconds: int32Ptr(1),
		}, {
			Name:           "webhook2.k8s.io",
			ClientConfig:   validClientConfig,
			SideEffects:    &noSideEffect,
			TimeoutSeconds: int32Ptr(15),
		}, {
			Name:           "webhook3.k8s.io",
			ClientConfig:   validClientConfig,
			SideEffects:    &noSideEffect,
			TimeoutSeconds: int32Ptr(30),
		},
		}, true),
	}, {

					Name:     utilpointer.StringPtr("http"),
					Port:     utilpointer.Int32Ptr(80),
					Protocol: &tcpProtocol,
				}, {
					Name:     utilpointer.StringPtr("https"),
					Port:     utilpointer.Int32Ptr(443),
					Protocol: &tcpProtocol,
				}, {
					Name:     utilpointer.StringPtr("extra1"),
					Port:     utilpointer.Int32Ptr(3000),
					Protocol: &tcpProtocol,
				}, {

      mlir::TF::IfOp op, const std::vector<int32_t>& operands,
      const std::vector<int32_t>& results);

  // Build while operator where cond & body are regions.
  std::optional<BufferOffset<tflite::Operator>> BuildWhileOperator(
      mlir::TFL::WhileOp op, const std::vector<int32_t>& operands,
      const std::vector<int32_t>& results);

  // Build call once operator.

  SmallVector<int64_t> InferPaddedTensorShape(
      const ArrayRef<int64_t> tensor_shape,
      const ArrayRef<int32_t> tfl_pad_values) const {
    SmallVector<int64_t> padded_shape(tensor_shape.begin(), tensor_shape.end());
    for (int i = 0; i < padded_shape.size(); ++i) {
      // Left padding + right padding.
      int32_t padding = tfl_pad_values[i * 2] + tfl_pad_values[i * 2 + 1];
      padded_shape[i] += padding;
    }

    // Add reshape op to be aligned with the input restriction with
    // TFL::resize_nearest_neighor op.
    const int32_t image_size = static_cast<int32_t>(params_type.getShape()[0]);
    const int32_t feature_size =
        static_cast<int32_t>(params_type.getShape()[3]);
    SmallVector<int32_t, 4> reshape_shape(
        {1, image_size, image_size, feature_size});
    SmallVector<int64_t, 4> reshape_shape_in_int64(

  llvm::SmallVector<int32_t, 4> flattened_out_segids =
      llvm::SmallVector<int32_t, 4>(operand_rank, static_cast<int32_t>(-1));
  for (int64_t i : dot_dimensions_info.out_dimensions().AxesArray()) {
    flattened_out_segids[i] = 0;
  }
  llvm::SmallVector<int32_t, 4> flattened_contracting_segids =
      llvm::SmallVector<int32_t, 4>(operand_rank, static_cast<int32_t>(-1));

  int32_t concat_dim = (*concat_dim_attr.getValues<int32_t>().begin());
  if (concat_dim < 0) concat_dim += num_dims;
  if (concat_dim >= num_dims || concat_dim < 0) return failure();

  // Check all elements besides at concat_dim match across all shape tensors.
  SmallVector<int32_t, 4> shape0;
  shape0.reserve(num_dims);

    MutableArrayRef<int64_t> input_shape, ArrayRef<int64_t> sparse_begin,
    ArrayRef<int64_t> sparse_end, ArrayRef<int64_t> sparse_strides,
    int32_t begin_mask, int32_t end_mask, int32_t ellipsis_mask,
    int32_t new_axis_mask, int32_t shrink_axis_mask,
    SmallVectorImpl<int64_t> *begin, SmallVectorImpl<int64_t> *end,
    SmallVectorImpl<int64_t> *stride) {
  int64_t num_sparse_indices = sparse_begin.size();

  absl::flat_hash_map<int32_t, int32_t> permutation_map;

  for (size_t before_dim_idx = 0, after_dim_idx = 0;
       before_dim_idx < input_shape.size(); ++before_dim_idx) {
    if (input_shape[before_dim_idx] == 1) {
      continue;
    }
    permutation_map.insert({before_dim_idx, after_dim_idx++});
  }

  SmallVector<int32_t> squeezed_permutation;
  DenseElementsAttr input_perm_const;

	// cond: !config.BigEndian
	// result: (LoweredAtomicOr (AND <typ.UInt32Ptr> (MOVWconst [^3]) ptr) (SLL <typ.UInt32> (ZeroExt8to32 val) (SLLconst <typ.UInt32> [3] (ANDconst <typ.UInt32> [3] ptr))) mem)
	for {
		ptr := v_0
		val := v_1
		mem := v_2
		if !(!config.BigEndian) {
			break
		}
		v.reset(OpMIPSLoweredAtomicOr)
		v0 := b.NewValue0(v.Pos, OpMIPSAND, typ.UInt32Ptr)
		v1 := b.NewValue0(v.Pos, OpMIPSMOVWconst, typ.UInt32)