// 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();

                               "'");
      TypeAttr type_attr = mlir::dyn_cast<TypeAttr>(attr);
      if (!type_attr)
        return InvalidArgument("Attribute '", output_arg.type_attr(),
                               "' required for output '", output_arg.name(),
                               "' isn't a type attribute");
      state_->types.push_back(UnrankedTensorType::get(type_attr.getValue()));
    } else if (!output_arg.type_list_attr().empty()) {

    if (matchPattern(cst_tensor_op.getArg(), m_Constant(&array))) {
      llvm::DenseSet<Type> all_types;
      for (auto it : array) {
        TypedAttr typed_attr = it.dyn_cast<TypedAttr>();
        if (!typed_attr) return failure();
        all_types.insert(typed_attr.getType());
      }
      if (all_types.size() != 1) return failure();
      ShapedType new_out_type = RankedTensorType::get(

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());

  llvm::SmallVector<Attribute, 2> shape_attrs;
  llvm::SmallVector<Attribute, 2> type_attrs;
  for (Type type : dataset_types) {
    shape_attrs.push_back(
        TF::ShapeAttr::get(builder.getContext(), mlir::cast<ShapedType>(type)));
    type_attrs.push_back(TypeAttr::get(getElementTypeOrSelf(type)));
  }

  auto anonymous_iterator = builder.create<AnonymousIteratorV3Op>(

meta_graphs {
  meta_info_def {
    stripped_op_list {
      op {
        name: "Add"
        input_arg {
          name: "x"
          type_attr: "T"
        }
        input_arg {
          name: "y"
          type_attr: "T"
        }
        output_arg {
          name: "z"
          type_attr: "T"
        }
        attr {
          name: "T"
          type: "type"
          allowed_values {
            list {