// Returns element type from attribute Type 'type_attr'.
mlir::Type GetShapeStrippedType(mlir::TypeAttr type_attr);

// Returns true if 'val' is not from Quantize op or
// from Quantize Op with same quant type as 'qtype_attr'
bool NotFromQuantOpOrSameQuantType(mlir::Value val, mlir::TypeAttr qtype_attr);

}  // namespace tflite

  }
}

mlir::Type GetShapeStrippedType(mlir::TypeAttr type_attr) {
  auto type = type_attr.getValue();
  auto shaped_type = mlir::dyn_cast<mlir::ShapedType>(type);
  if (shaped_type) {
    return shaped_type.getElementType();
  } else {
    return type;
  }
}

bool NotFromQuantOpOrSameQuantType(mlir::Value val, mlir::TypeAttr qtype_attr) {
  auto val_defn_op = val.getDefiningOp();

Adrian Kuegel <******@****.***> 1648469008 -0700

Tres Popp <******@****.***> 1648106860 -0700

            dequantize_input.getType(), dequantize_op.getLoc());
        // replace the dequantize op by a quantize op
        TypeAttr type_attr = TypeAttr::get(returned_type);
        auto quantize_op = builder.create<QuantizeOp>(
            dequantize_op.getLoc(), returned_type, dequantize_input, type_attr);
        returned_value = quantize_op.getOutput();
      } else {
        output_type.print(llvm::errs() << "Requested output type ");

    builder.setInsertionPoint(&block, ++Block::iterator(op));
  } else {
    builder.setInsertionPointToStart(&block);
  }
  TypeAttr type_attr = TypeAttr::get(new_type);
  auto quantize = builder.create<TFL::QuantizeOp>(value.getLoc(), new_type,
                                                  value, type_attr);
  auto dequantize = builder.create<TFL::DequantizeOp>(
      value.getLoc(), expressed_type, quantize.getOutput());

#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/platform/status.h"
#include "tsl/platform/statusor.h"

namespace mlir {
namespace TFL {

absl::StatusOr<TypedAttr> CreateTypedAttr(ShapedType shaped_type, int value) {
  Type element_type = shaped_type.getElementType();
  if (element_type.isF16()) {
    auto floatType = mlir::FloatType::getF16(element_type.getContext());

  Status status;
  NodeDef def;
  def.set_op("Bitcast");
  def.set_device(DEVICE_CPU);

  AttrValue typeAttr;
  SetAttrValue(input_tensor->dtype(), &typeAttr);

  AttrValue outTypeAttr;
  SetAttrValue(out_type, &outTypeAttr);

  (*def.mutable_attr())["T"] = typeAttr;
  (*def.mutable_attr())["type"] = outTypeAttr;

  def.add_input(

}

/// Returns whether the given `a` and `b` have broadcast-compatible
/// types.
bool IsBroadcastableElementsAttrs(mlir::TypedAttr a, mlir::TypedAttr b);
// Returns true if every dimension of the attribute is 1 except the last one.
bool IsDimensionsDegenerateExceptLastOne(mlir::TypedAttr val);
// Returns true if every element is 1 except the last one.
bool IsDimensionsDegenerateExceptLastOne(ArrayRef<int64_t> elements_shape);

  return !std::any_of(elements.begin(), elements.end(), [](Attribute e) {
    return mlir::cast<IntegerAttr>(e).getValue() != 1;
  });
}

bool IsBroadcastableElementsAttrs(mlir::TypedAttr a, mlir::TypedAttr b) {
  // This would return false if we had unranked tensors (where they should
  // probably be considered as broadcastable), but given we are working with
  // attributes here that shouldn't be an issue,