// Get a DenseIntElementsAttr of type I64 and convert it to an I32 attribute.
DenseIntElementsAttr DenseI64AttrToI32Attr(
    const DenseIntElementsAttr& dense_attr, PatternRewriter& builder);

// Returns a NHWC shaped type from an NCHW shaped type op.
// For example- Given a Composite op that wraps a core.aten.avg_pool2d, this
// returns the return type of the tfl.average_pool_2d emitted. Note that the

#include "mlir/IR/Value.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project

namespace mlir {
namespace odml {

DenseIntElementsAttr DenseI64AttrToI32Attr(
    const DenseIntElementsAttr& dense_attr, PatternRewriter& builder) {
  std::vector<int32_t> ret(dense_attr.getNumElements());
  auto range = dense_attr.getValues<int64_t>();

// Receives a composite DictionaryAttr and returns the value of the Attribute
// with the key `attr_name` as a DenseIntElementsAttr.
class GetAsVectorAttr<string attr_name>:
  GetCompositeAttributeAs<attr_name, "DenseIntElementsAttr">;

class IsBoolAttrEqual<string true_or_false> : Constraint<CPred<
  "$0.getValue() == "#true_or_false#"">>;

      new_shape[i] = shape[permutation[i]];

    return RankedTensorType::get(new_shape, ranked_type.getElementType());
  }

  return type;
}

bool AreCancellablePermutations(DenseIntElementsAttr perm0,
                                DenseIntElementsAttr perm1) {
  if (perm0.getNumElements() == 0 || perm1.getNumElements() == 0) return false;
  if (perm0.getNumElements() != perm1.getNumElements()) return false;

// 2. IotaOp + BroadCastInDim.
// 3. IotaOp + Reshape.
// 4. Constant (folded Iota) + BroadCastInDim.
// 5. Constant (folded result).
// Moreover, the dimensions has to match the iota_dimension.
bool MatchIota(DenseIntElementsAttr dimensions, Value iota);
}  // namespace odml
}  // namespace mlir

namespace odml {

// Given a Composite op that wraps a core.aten.avg_pool2d, returns the padding
// configuration required for the `tfl.pad` if the padding part of the op is
// to be done before average pooling.
DenseIntElementsAttr GetPadOpAttr(Builder& builder, mhlo::CompositeOp op);

// Given a Composite op that wraps a core.aten.avg_pool2d, and assuming that
// the padding part is extracted into a tfl.pad op prior to a

    Operation *bcast_or_const_op;
    shape::ShapeOfOp shape_of_op;
    mhlo::ConstantOp filter;
    mhlo::ConstantOp multiplier;
    mlir::ElementsAttr filter_value, mul_value;
    mlir::DenseIntElementsAttr broadcast_dims;

    // Match and capture values/attributes.
    Value lhs = mul_op.getLhs();
    Value rhs = mul_op.getRhs();
    conv_op = lhs.getDefiningOp<mhlo::ConvolutionOp>();
    if (conv_op == nullptr) {

    DenseI64ArrayAttr new_win_dilations =
        getInvertPermutedAttr(reduce_op.getWindowDilations());

    auto padding = reduce_op.getPadding();
    int64_t rank = transpose_perm.size();
    DenseIntElementsAttr new_padding_attr = nullptr;
    if (padding.has_value()) {
      SmallVector<int64_t> new_padding(rank * 2, 0);
      auto old_padding = (*padding).getValues<int64_t>();
      for (int64_t idx = 0; idx < rank; ++idx) {

    if (bmm_op.getAdjX()) {
      rY = rhs_shape.size() - 2;
      cY = rhs_shape.size() - 1;
    }

    auto reduce_dim_op = rewriter.create<TFL::ConstOp>(
        bmm_op->getLoc(),
        DenseIntElementsAttr::get(
            RankedTensorType::get({1}, rewriter.getI32Type()), {cY}));
    auto sum_op = rewriter.create<TFL::SumOp>(
        bmm_op->getLoc(), bmm_op.getType(), bmm_op.getY(), reduce_dim_op,

        float_attr.getSplatValue<APFloat>().isExactlyValue(raw_value))
      return true;
  } else if (mlir::isa<IntegerType>(element_type)) {
    DenseIntElementsAttr int_attr;
    if (matchPattern(value, m_Constant(&int_attr)) && int_attr.isSplat() &&
        int_attr.getSplatValue<APInt>() == raw_value)
      return true;
  }

  return false;
}