}
	return lockNames[rank]
}
`)

	// Create partial order structure.
	fmt.Fprintf(w, `
// lockPartialOrder is the transitive closure of the lock rank graph.
// An entry for rank X lists all of the ranks that can already be held
// when rank X is acquired.
//
// Lock ranks that allow self-cycles list themselves.
var lockPartialOrder [][]lockRank = [][]lockRank{
`)
	for _, rank := range topo {

namespace tensorflow {

// Set the shapes and types of the output's handle.
//
// The lengths of the arrays pointed to by `shapes`, `ranks`, and `types` must
// all be equal to `num_shapes_and_types`. If `ranks[i] != -1`, (i.e., if the
// rank is known), then it must be equal to the length of `shapes[i]`; if
// `ranks[i] == 1`, then `shapes[i]` may be nullptr.
//
// TODO(akshayka): Implement a corresponding getter method.

    if (auto result_constraint = results.GetConstraint(op->getResult(0))) {
      // `test.OpA` converts shape constraint to rank constraint.
      if (is_op_a && *result_constraint == ValueConstraint::kShape)
        operands.Insert(op->getOperand(0), ValueConstraint::kRank);

      // `test.OpB` converts value constraint to shape constraint.
      if (*result_constraint == ValueConstraint::kValue)

      current_indices.push_back(i);
      TransposeRecursively(original_values, target_values, current_indices);
      current_indices.pop_back();
    }
  }

  int rank_;                             // Rank of the input values.
  SmallVector<int64_t> original_shape_;  // Shape of the original tensor.
  SmallVector<int64_t> target_shape_;    // Shape of the target tensor.
  SmallVector<int64_t> permutation_;
};

<component name="ProjectDictionaryState">
  <dictionary name="Nikolay.Krasko">
    <words>
      <w>accessors</w>
      <w>codeowners</w>
      <w>coroutines</w>
      <w>crossinline</w>
      <w>fqname</w>
      <w>goto</w>
      <w>gradle</w>
      <w>infos</w>
      <w>intrinsics</w>
      <w>kdoc</w>
      <w>lateinit</w>
      <w>memoize</w>
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      <w>multiline</w>
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}

// Returns true if the given `value` has at least the specified rank or has
// unranked type.
inline bool HasRankAtLeast(Value value, int64_t rank) {
  RankedTensorType type = GetRankedTensorTypeForOperand(value);
  return !type || type.getRank() >= rank;
}

// Returns true if the given `value` has at most the specified rank or has
// unranked type.
inline bool HasRankAtMost(Value value, int64_t rank) {

        SmallVector<int64_t, 4>(rank, ShapedType::kDynamic), element_ty);
  }

  int64_t num_reduce_dim = 0;
  llvm::SmallVector<bool, 4> is_reduce_dim(rank, false);
  for (const APInt &index : indices.getValues<APInt>()) {
    int64_t dim = GetDimForAxis(index.getSExtValue(), rank);
    // Invalid input.
    if (dim < 0 || dim >= rank) return UnrankedTensorType::get(element_ty);

    if (!is_reduce_dim[dim]) {
      is_reduce_dim[dim] = true;

        }

        final ArtifactStatus that = (ArtifactStatus) o;

        return rank == that.rank;
    }

    public int hashCode() {
        return rank;
    }

    public String toString() {
        return key;
    }

    public int compareTo(ArtifactStatus s) {
        return rank - s.rank;
    }

typedef struct TF_Shape TF_Shape;

// Return a new, unknown rank shape object. The caller is responsible for
// calling TF_DeleteShape to deallocate and destroy the returned shape.
TF_CAPI_EXPORT extern TF_Shape* TF_NewShape();

// Returns the rank of `shape`. If `shape` has unknown rank, returns -1.
TF_CAPI_EXPORT extern int TF_ShapeDims(const TF_Shape* shape);

    this.rankMap = rankMap;
  }

  @Override
  public int compare(T left, T right) {
    return rank(left) - rank(right); // safe because both are nonnegative
  }

  private int rank(T value) {
    Integer rank = rankMap.get(value);
    if (rank == null) {
      throw new IncomparableValueException(value);
    }
    return rank;
  }

  @Override
  public boolean equals(@CheckForNull Object object) {