return (emitError(loc, "unsupported FakeQuant number of bits: ") << numBits,
            nullptr);
  }

  SmallVector<double, 4> scales;
  SmallVector<int64_t, 4> zeroPoints;
  scales.reserve(axisSize);
  zeroPoints.reserve(axisSize);
  for (size_t axis = 0; axis != axisSize; ++axis) {
    double rmin = rmins[axis];
    double rmax = rmaxs[axis];
    if (std::fabs(rmax - rmin) < std::numeric_limits<double>::epsilon()) {

    }
    if (argument.HasDefaultValue()) {
      args_with_default_val.push_back(text);
    } else {
      args_without_default_val.push_back(text);
    }
  }
  std::vector<string> arguments;
  arguments.reserve(args_without_default_val.size() +
                    args_with_default_val.size());
  arguments.insert(arguments.end(),
                   std::make_move_iterator(args_without_default_val.begin()),

}

// varintAt writes varint v at byte position pos in buf. This always
// consumes traceBytesPerNumber bytes. This is intended for when the caller
// needs to reserve space for a varint but can't populate it until later.
// Use varintReserve to reserve this space.
func (buf *traceBuf) varintAt(pos int, v uint64) {
	for i := 0; i < traceBytesPerNumber; i++ {
		if i < traceBytesPerNumber-1 {
			buf.arr[pos] = 0x80 | byte(v)

  return var_handle_op.getSharedName().str();
}

std::vector<std::string> GetVariableNames(
    llvm::ArrayRef<TF::VarHandleOp> var_handle_ops) {
  std::vector<std::string> names;
  names.reserve(var_handle_ops.size());
  for (auto var_handle_op : var_handle_ops)
    names.push_back(GetVariableName(var_handle_op));
  return names;
}

tensorflow::Var* GetVariableFromSession(mlir::TF::VarHandleOp var_handle_op,

  if (!attr) return success();

  // ID pairs are separated by `,`.
  llvm::SmallVector<std::string, 8> str_list =
      absl::StrSplit(attr.getValue().str(), ',', absl::SkipWhitespace());
  id_pairs.reserve(str_list.size());
  for (const std::string& str : str_list) {
    // IDs of one pair are separated by `:`.
    llvm::SmallVector<std::string, 8> id_pair = absl::StrSplit(str, ':');

    // Check for malformed IDs.

  std::size_t dim_size = type.getDimSize(quantization_dim_);
  if (dim_size != scales_.size()) {
    return {};
  }
  SmallVector<UniformQuantizedValueConverter, 4> converters;
  converters.reserve(dim_size);
  for (int i = 0, e = dim_size; i != e; ++i) {
    converters.push_back(getPerChunkConverter(i));
  }

  // Scan the elements of the dense elements attributes and quantize them by

#include "xla/client/local_client.h"

namespace tensorflow {
namespace {
std::vector<const xla::Shape*> GetShapePointers(
    absl::Span<const xla::Shape> shapes) {
  std::vector<const xla::Shape*> shape_ptrs;
  shape_ptrs.reserve(shapes.size());
  for (const auto& shape : shapes) {
    shape_ptrs.push_back(&shape);
  }
  return shape_ptrs;
}
}  // namespace

absl::StatusOr<std::unique_ptr<xla::LocalExecutable>>

    // non-quantized int64
    if (allTypesFp || allTypesQuantizedOrInt) return failure();

    Location loc = op->getLoc();
    SmallVector<Value> newOperands;
    newOperands.reserve(op->getNumOperands());
    for (auto operand : op->getOperands()) {
      if (QuantizedType::getQuantizedElementType(operand.getType())) {
        auto newTy = QuantizedType::castToExpressedType(operand.getType());

}

const maxlines = 64 * 1024

func (s *fakeFileSet) pos(file string, line, column int) token.Pos {
	// TODO(mdempsky): Make use of column.

	// Since we don't know the set of needed file positions, we reserve
	// maxlines positions per file. We delay calling token.File.SetLines until
	// all positions have been calculated (by way of fakeFileSet.setLines), so
	// that we can avoid setting unnecessary lines. See also golang/go#46586.

  mlir::MLIRContext* ctx = op->getContext();
  mlir::Builder builder(ctx);

  // Collect devices with attached metadata.
  llvm::SmallVector<mlir::NamedAttribute, 8> devices;
  devices.reserve(device_set->devices().size());

  // For device that do not have any metadata, or if we failed to parse metadata
  // from the DeviceSet, we add a unit attribute to the `tf.devices` attribute.