namespace mlir {
namespace odml {

bool IsIotaAttr(ArrayRef<int64_t> arr, int64_t size) {
  if (arr.size() != size) return false;
  int64_t iota = 0;
  for (auto s : arr) {
    if (s != iota) return false;
    ++iota;
  }
  return true;
}

PermutationAndShape GetPermutationAndTransposedShape(
    llvm::ArrayRef<int64_t> permutation_array, ShapedType input_type,

// should have the same size.
// Example: Index (2, 3) of a (4, 5)-shaped tensor has the contiguous offset of
// 2 * 5 + 3 = 13.
int64_t GetContiguousOffset(const ArrayRef<int64_t> indices,
                            const ArrayRef<int64_t> shape) {
  int64_t contiguous_offset = 0;
  int64_t base_offset = 1;
  for (auto [i, dimension] : llvm::reverse(llvm::zip_equal(indices, shape))) {

}

Status BuildHloFromGraph(
    const Graph& graph, xla::XlaBuilder& builder,
    mlir::MLIRContext& mlir_context, llvm::ArrayRef<xla::XlaOp> xla_params,
    std::vector<xla::XlaOp>& returns, bool unconditionally_use_output_shapes,
    llvm::ArrayRef<XlaArgument> args, llvm::ArrayRef<std::string> control_rets,
    llvm::StringRef device_type, const FunctionLibraryDefinition& flib_def,
    const GraphDebugInfo& debug_info,

Status BuildHloFromGraph(
    const Graph& graph, xla::XlaBuilder& builder,
    mlir::MLIRContext& mlir_context, llvm::ArrayRef<xla::XlaOp> xla_params,
    std::vector<xla::XlaOp>& returns, bool unconditionally_use_output_shapes,
    llvm::ArrayRef<XlaArgument> args, llvm::ArrayRef<std::string> control_rets,
    llvm::StringRef device_type, const FunctionLibraryDefinition& flib_def,
    const GraphDebugInfo& debug_info,

#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"

namespace mlir {
namespace odml {
namespace {
// A struct to hold axes and sizes for a set of dimensions.
struct DimensionVector {
  llvm::ArrayRef<int64_t> AxesArray() const { return axes; }
  llvm::ArrayRef<int64_t> SizesArray() const { return sizes; }

  llvm::SmallVector<int64_t, 4> axes;
  llvm::SmallVector<int64_t, 4> sizes;
};

// Appends all elements in `range` to `values`.

// The following two functions are only used for mapping/excluding attributes
// which are inconsistent between TFG and TFE.
//
static mlir::LogicalResult FilterTfgSpecificArgResultAttributes(
    mlir::MLIRContext *context, mlir::ArrayRef<Type> types,
    mlir::ArrayAttr array_attr, llvm::SmallVector<mlir::Type> &output_types,
    llvm::SmallVector<mlir::DictionaryAttr> &output_attrs) {
  for (auto it : llvm::zip(

// Facilitate access to 1-d backing data for a tensor so that values in a 1-d
// slice of the tensor can be accessed as if part of an ArrayView.
class StridedArrayViewBase {
 protected:
  StridedArrayViewBase(ArrayRef<int64_t> shape, ArrayRef<int64_t> index,
                       int64_t axis) {
    assert(shape.size() == index.size());
    assert(axis < shape.size());
    assert(axis >= 0);
    assert(index[axis] == 0);

                                         StringRef func_name,
                                         ArrayRef<Value> arguments,
                                         ArrayRef<Value> results,
                                         ArrayRef<NamedAttribute> attributes);

// Same as above but with empty attributes.
SmallVector<Value, 4> LiftAsFunctionCall(OpBuilder& builder, Location location,

 public:
  using TensorflowDataflowAnalysis<
      ResourceConstructingOps>::TensorflowDataflowAnalysis;
  void visitOperation(Operation *op, ArrayRef<const StateT *> operands,
                      ArrayRef<StateT *> results) override {
    if (ForwardThroughTFOperation(op, operands, results)) return;
    setAllToEntryStates(results);
  }
  ~ResourceDataflowAnalysis() override = default;
};

                                          axis_op);
}

ArrayRef<int64_t> GetRankedTensorShape(Value value) {
  return mlir::cast<RankedTensorType>(value.getType()).getShape();
}

Value SliceRankedTensor(OpBuilder* builder, Value input,
                        ArrayRef<int64_t> begin_shape,
                        ArrayRef<int64_t> begin_values,
                        ArrayRef<int64_t> size_shape,