DenseFPElementsAttr::get(scale_type,
                               {static_cast<float>(qtype.getScale())}));
  zero_point = rewriter.create<TF::ConstOp>(
      loc, zero_point_type,
      DenseIntElementsAttr::get(zero_point_type,
                                {static_cast<int32_t>(qtype.getZeroPoint())}));
  return success(scale && zero_point);
}

LogicalResult CreateUniformQuantizedPerAxisTypeParams(

        return transpose_op.emitOpError(
            "perm tensor elements size is not equal to input tensor rank");
      }
    }

    // Get permutation axes of the TransposeOp
    DenseIntElementsAttr perm;
    if (!matchPattern(transpose_op.getPerm(), m_Constant(&perm))) {
      return failure();
    }

    SmallVector<int64_t, 4> axes;
    for (const auto& axis_int : perm.getValues<APInt>()) {

      mlir::TensorType ty =
          mlir::RankedTensorType::get(vals.size(), builder.getIntegerType(64));
      TensorProto tensor;
      TF_RETURN_IF_ERROR(ConvertToTensorProto(
          mlir::DenseIntElementsAttr::get(ty, vals), &tensor));
      *list->add_tensor() = tensor;
    } else {
      return errors::Unimplemented("Unhandled attribute!");
    }
  }
  return absl::OkStatus();
}

namespace tensorflow {
namespace tf2xla {
namespace internal {

namespace {

using llvm::ArrayRef;
using llvm::SmallVector;
using mlir::Block;
using mlir::BlockArgument;
using mlir::DenseIntElementsAttr;
using mlir::IRMapping;
using mlir::Location;
using mlir::LogicalResult;
using mlir::ModuleOp;
using mlir::OpBuilder;
using mlir::Operation;
using mlir::OperationPass;
using mlir::OpOperand;

                               OpBuilder* builder) {
  auto type =
      mlir::RankedTensorType::get(value.size(), builder->getIntegerType(32));
  auto dense_values = mlir::DenseIntElementsAttr::get(type, value);
  return builder->create<TF::ConstOp>(loc, dense_values);
}

TF::ConstOp CreateScalarConstantOp(int value, Location loc,
                                   OpBuilder* builder) {

              });
    } else if (isa<stablehlo::ConvertOp>(filter_op) &&
               isa<stablehlo::ConstantOp>(
                   filter_op->getOperand(0).getDefiningOp())) {
      filter_i8_value_attr = mlir::cast<DenseIntElementsAttr>(
          cast<stablehlo::ConstantOp>(filter_op->getOperand(0).getDefiningOp())
              .getValue());
    }

    // Create Uniform Quantized constant for the filter.
    auto subtract_op =

                                          builder));
  }
  if (op_name == "tfl.reshape") {
    // Flattens reshape ops when more than one dimension shape operand is given.
    mlir::DenseIntElementsAttr shape_attr;
    if (matchPattern(op_state.operands[1], m_Constant(&shape_attr))) {
      auto shape_ty =
          op_state.operands[1].getType().dyn_cast<RankedTensorType>();

       llvm::make_early_inc_range(block->getOps<TF::VarIsInitializedOp>())) {
    builder.setInsertionPoint(op);
    if (!const_true)
      const_true = builder.create<TF::ConstOp>(
          op.getLoc(),
          DenseIntElementsAttr::get(
              RankedTensorType::get(/*shape=*/{}, builder.getI1Type()), true));

    op.getIsInitialized().replaceAllUsesWith(const_true);
    op.erase();
  }
}