}

// Shuffle ranked tensor dimensions according to the permutation.
Type ShuffleRankedTensorType(Type type, ArrayRef<int64_t> permutation) {
  if (auto ranked_type = mlir::dyn_cast<RankedTensorType>(type)) {
    ArrayRef<int64_t> shape = ranked_type.getShape();
    assert(permutation.size() == shape.size());

    SmallVector<int64_t, 4> new_shape(permutation.size());
    for (size_t i = 0; i < permutation.size(); ++i)

// Handles padding before convolution for space to depth transform.
LogicalResult HandlePad(TF::PadOp op, int32_t kernel_size, int32_t block_size) {
  auto ranked_type = mlir::dyn_cast<RankedTensorType>(op.getInput().getType());
  if (!ranked_type) return failure();
  auto pad_input_shape = ranked_type.getShape();
  Location loc = op.getLoc();
  OpBuilder builder(op);
  builder.setInsertionPoint(op);

    } else {
      return UnrankedTensorType::get(builder->getI1Type());
    }
  }

  auto ranked_type = mlir::dyn_cast<RankedTensorType>(result_type);
  if (!ranked_type) return UnrankedTensorType::get(builder->getI1Type());

  return RankedTensorType::get(ranked_type.getShape(), builder->getI1Type());
}

Type InferReductionOpType(Value input, Value reduction_indices,

}

// Converts the condition for an IfOp/WhileOp to a boolean value.
Value ConvertConditionToBoolean(Operation* op, Value cond) {
  if (auto ranked_type = mlir::dyn_cast<RankedTensorType>(cond.getType()))
    if (ranked_type.getRank() == 0 &&
        ranked_type.getElementType().isSignlessInteger(1))
      return cond;

  OpBuilder builder(op);
  Value to_bool = builder.create<TF::ToBoolOp>(op->getLoc(), cond);

    if (dim != 1) return false;
  }
  return true;
}

bool IsDimensionsDegenerateExceptLastOne(TypedAttr val) {
  if (auto ranked_type = mlir::dyn_cast<RankedTensorType>(val.getType())) {
    return IsDimensionsDegenerateExceptLastOne(ranked_type.getShape());
  }
  return false;
}

}  // namespace TFL

      elem_type.isF32()) {
    zero = builder.getFloatAttr(elem_type, 0);
  } else {
    return false;
  }
  if (auto ranked_type = dyn_cast<RankedTensorType>(type)) {
    llvm::ArrayRef<int64_t> type_shape = ranked_type.getShape();
    for (int64_t i : type_shape) {
      if (i < 0) return false;
    }
    shape = builder.getI64TensorAttr(type_shape);
  } else {
    return false;

    // verify shardings that actually break a tensor apart.
    return mlir::success();
  }
  if (RankedTensorType ranked_type = mlir::dyn_cast<RankedTensorType>(type)) {
    const int64_t tensor_rank = ranked_type.getRank();
    int tile_assignment_rank = sharding->tile_assignment_dimensions_size();

    // When a tensor is partial or subgroup tiled, its tile assignment will

                                 Type& shard_type) {
  RankedTensorType ranked_type = mlir::dyn_cast<RankedTensorType>(full_type);
  if (!ranked_type)
    return context_op->emitOpError()
           << "A map_outside_compilation op's input and output types must be "
              "ranked tensors.";
  ArrayRef<int64_t> in_shape = ranked_type.getShape();
  if (in_shape.empty() || in_shape[0] < 0) {

    // also populate the shape info to the op result. The shape of the
    // tensorlist is inferred from `num_elements` and `element_shape`.
    auto ranked_type = element_shape.getType().dyn_cast<RankedTensorType>();
    DenseIntElementsAttr dense_elem_attr;
    if ((ranked_type && ranked_type.getRank() == 0) ||
        !matchPattern(element_shape, m_Constant(&dense_elem_attr))) {
      // If no constant is spotted, just forward the operand.

RankedTensorType DropFirstDimension(Type type) {
  RankedTensorType ranked_type = mlir::dyn_cast<RankedTensorType>(type);
  if (!ranked_type) return {};
  llvm::ArrayRef<int64_t> dims_except_first =
      ranked_type.getShape().drop_front();
  return tensorflow::GetTypeFromTFTensorShape(dims_except_first,
                                              ranked_type.getElementType());
}