AttrValue attr_val;
      // For type attributes, we only propagate the element type.
      mlir::Type elt_type = attr.getValue();
      if (auto shaped_type = mlir::dyn_cast<mlir::ShapedType>(elt_type)) {
        elt_type = shaped_type.getElementType();
      }
      TF_RETURN_IF_ERROR(
          ConvertAttribute(elt_type, remove_ref_type, &attr_val));
      list->add_type(attr_val.type());

  node_def->set_op(FunctionLibraryDefinition::kRetOp);
  DataType dtype;
  TF_RETURN_IF_ERROR(ConvertToDataType(
      mlir::cast<mlir::TensorType>(operand.getType()).getElementType(),
      &dtype));
  AttrValue type_attr;
  type_attr.set_type(dtype);
  (*node_def->mutable_attr())["T"] = type_attr;
  AttrValue index_attr;
  index_attr.set_i(index);
  (*node_def->mutable_attr())["index"] = index_attr;

  node_def->set_op(FunctionLibraryDefinition::kRetOp);
  DataType dtype;
  TF_RETURN_IF_ERROR(ConvertToDataType(
      mlir::cast<mlir::TensorType>(operand.getType()).getElementType(),
      &dtype));
  AttrValue type_attr;
  type_attr.set_type(dtype);
  (*node_def->mutable_attr())["T"] = type_attr;
  AttrValue index_attr;
  index_attr.set_i(index);
  (*node_def->mutable_attr())["index"] = index_attr;

  const auto& input_dtype = input_tensor.dtype();
  const auto& input_shape = input_tensor.shape();
  Type elt_type;
  TF_RETURN_IF_ERROR(ConvertDataType(input_dtype, *builder, &elt_type));
  SmallVector<int64_t, 4> shape;
  ConvertToMlirShape(input_shape, &shape);
  auto type = RankedTensorType::get(shape, elt_type);

#define CONVERT_FLAT(DTYPE, CTYPE) \
  case DTYPE:                      \

  return absl::OkStatus();
}

Status MlirAbstractOp::AddRef(Type type, Type* output_type) {
  Type elt_type = getElementTypeOrSelf(type);
  if (mlir::isa<mlir::TF::TensorFlowRefType>(elt_type)) {
    return InvalidArgument("Requested reference to a reference type");
  }
  elt_type = TensorFlowRefType::get(elt_type);
  if (RankedTensorType tensor_type = mlir::dyn_cast<RankedTensorType>(type)) {

    for (auto [call_arg, body_arg] :
         llvm::zip(call.getOperation()->getOperands(),
                   f.getBody().front().getArguments())) {
      if (call_arg.getType() != body_arg.getType()) {
        body_arg.setType(call_arg.getType());
        changed = true;
      }
    }
    if (changed) {
      rewriter.finalizeOpModification(f);
      return success();
    } else {

      arg.setType(ReplaceElementType(arg.getType(), element_type));
    }

    for (Operation& op : region.getOps()) {
      for (Value operand : op.getOperands()) {
        operand.setType(ReplaceElementType(operand.getType(), element_type));
      }

      for (Value result : op.getResults()) {
        result.setType(ReplaceElementType(result.getType(), element_type));
      }
    }
  }

    op.getResult().setType(conv_result_type);

    squeeze_op.getResult().setType(bts_op.getOutput().getType());

    // Connect `biasadd_op` with the output of `squeeze_op`.
    if (biasadd_op) {
      biasadd_op.setOperand(0, squeeze_op.getOutput());
      biasadd_op.getOutput().setType(squeeze_op.getOutput().getType());
    }
  } else {
    if (biasadd_op) biasadd_op.setOperand(0, op.getOutput());

    auto ranked_type = mlir::dyn_cast<RankedTensorType>(input.getType());
    if (!ranked_type) return false;
    auto input_shape = ranked_type.getShape();
    auto space_to_depth =
        BuildSpaceToDepth(cluster_func, input, block_size, input_shape);
    entry.value().set(space_to_depth);
    block_arg.setType(space_to_depth.getType());
  }
  return true;
}

      Value handle = tensor_array_v3_op.getHandle();
      auto handle_type = mlir::cast<TensorType>(handle.getType());
      if (handle_type.getElementType().isInteger(/*width=*/32)) {
        Type resource_tensor_type =
            handle_type.clone(TF::ResourceType::get(rewriter.getContext()));
        handle.setType(resource_tensor_type);
      }
    }

    // Special type fixes for scalar tensor types.