inline DenseElementsAttr GetShape(Value output_val, bool truncate = false) {
  auto output_shape = output_val.getType().dyn_cast<ShapedType>().getShape();

  SmallVector<int32_t> shape;
  shape.reserve(output_shape.size());

  bool needs_truncation = true;
  for (size_t dim_idx = 0; dim_idx < output_shape.size(); ++dim_idx) {
    int64_t dim = output_shape[dim_idx];
    if (truncate && needs_truncation && dim == 1) {

	// Transition from C ABI to Go ABI.
	PUSH_REGS_HOST_TO_ABI0()

	// Set up ABIInternal environment: g in R14, cleared X15.
	get_tls(R12)
	MOVQ	g(R12), R14
	PXOR	X15, X15

	// Reserve space for spill slots.
	NOP	SP		// disable vet stack checking
	ADJSP   $24

	// Call into the Go signal handler
	MOVQ	DI, AX	// sig
	MOVQ	SI, BX	// info
	MOVQ	DX, CX	// ctx

    }
    return success();
  }
  void rewrite(TF::_XlaHostComputeMlirOp op,
               PatternRewriter& rewriter) const override {
    llvm::SmallVector<Attribute> shape_attrs;
    shape_attrs.reserve(op.getNumResults());
    for (Type ty : op.getResultTypes()) {
      shape_attrs.push_back(TF::ShapeAttr::get(rewriter.getContext(),
                                               mlir::cast<ShapedType>(ty)));
    }

				time.Sleep(time.Minute)
			}
		}()
	}
	// Now apply kube reserved and system reserved limits if required.
	if nc.EnforceNodeAllocatable.Has(kubetypes.SystemReservedEnforcementKey) {
		klog.V(2).InfoS("Enforcing system reserved on cgroup", "cgroupName", nc.SystemReservedCgroupName, "limits", nc.SystemReserved)

   */
  @CanIgnoreReturnValue
  public double acquire(int permits) {
    long microsToWait = reserve(permits);
    stopwatch.sleepMicrosUninterruptibly(microsToWait);
    return 1.0 * microsToWait / SECONDS.toMicros(1L);
  }

  /**
   * Reserves the given number of permits from this {@code RateLimiter} for future use, returning

}

void TF_LockVariableInfos(TF_VariableInfo** vars, int num_vars,
                          TF_Status* status) {
  std::vector<tensorflow::VariableInfo*> variable_ptrs;
  variable_ptrs.reserve(num_vars);
  for (int i = 0; i < num_vars; ++i) {
    variable_ptrs.push_back(&(vars[i]->var_info));
  }
  absl::Status cc_status = LockVariables(absl::MakeSpan(variable_ptrs));
  status->status = cc_status;
}

  // supports importing graphs w/ unregistered ops natively.
  GraphDef pruned_graph_def;
  if (specs.prune_unused_nodes) {
    std::vector<std::string> terminal_nodes;
    terminal_nodes.reserve(specs.outputs.size() + specs.inputs.size());
    for (const auto& output : specs.outputs) {
      terminal_nodes.push_back(std::string(ParseTensorName(output).node()));
    }

}

TEST(RamFileBlockCacheTest, BlockAlignment) {
  // Initialize a 256-byte buffer.  This is the file underlying the reads we'll
  // do in this test.
  const size_t size = 256;
  std::vector<char> buf;
  buf.reserve(size);
  for (int i = 0; i < size; i++) {
    buf.push_back(i);
  }
  // The fetcher just fetches slices of the buffer.
  auto fetcher = [&buf](const string& filename, size_t offset, size_t n,

  }
  return dnums;
}

DenseIntElementsAttr ConvertToDenseElementsAttr(ArrayAttr array_attr,
                                                PatternRewriter &rewriter) {
  SmallVector<int64_t> array;
  array.reserve(array_attr.size());
  for (auto elem : array_attr.getAsRange<IntegerAttr>()) {
    array.push_back(elem.getInt());
  }
  return DenseIntElementsAttr::get(

    // Note that we take the address of the elements in `all_input_tensors`
    // below. Allocate enough space so that no reallocation happens, which will
    // make the pointers invalid.
    all_input_tensors.reserve(num_inputs);
    for (int i = 0; i < num_inputs; ++i) {
      if (input_tensors[i] == nullptr) continue;
      all_input_tensors.emplace_back();
      Tensor& input_tensor = all_input_tensors.back();