Value none = rewriter.create<TFL::NoValueOp>(
      op->getLoc(), rewriter.getNoneType(), rewriter.getUnitAttr());

  Value output_shape =
      CreateCastToInt32(tf_op.getInputSizes(), op->getLoc(), rewriter);

  rewriter.replaceOpWithNewOp<TFL::Conv3DTransposeOp>(
      op, tf_op.getType(), output_shape, tf_op.getFilter(),
      tf_op.getOutBackprop(),
      /*bias=*/none, dilation_depth_factor, dilation_height_factor,

      (*node_def->mutable_attr())["_handle_dtypes"] = handle_dtypes_attr;
      (*node_def->mutable_attr())["_handle_shapes"] = handle_shapes_attr;
    }
  }

  TF_RETURN_IF_ERROR(
      SetShapeAttribute("_output_shapes", arg_type, node_def->mutable_attr()));

  DataType dtype;
  TF_RETURN_IF_ERROR(ConvertToDataType(arg_type.getElementType(), &dtype));
  AttrValue type_attr;
  type_attr.set_type(dtype);

      (*node_def->mutable_attr())["_handle_dtypes"] = handle_dtypes_attr;
      (*node_def->mutable_attr())["_handle_shapes"] = handle_shapes_attr;
    }
  }

  TF_RETURN_IF_ERROR(
      SetShapeAttribute("_output_shapes", arg_type, node_def->mutable_attr()));

  DataType dtype;
  TF_RETURN_IF_ERROR(ConvertToDataType(arg_type.getElementType(), &dtype));
  AttrValue type_attr;
  type_attr.set_type(dtype);

  // the shape inference pass is run early in the pass pipeline, shape inference
  // during import is not necessary.
  config.enable_shape_inference = false;
  // Some graphs may require _output_shapes (an unregistered attribute)
  // to override shapes. It is unfortunately not always set correctly so only
  // do it optionally.
  config.unconditionally_use_set_output_shapes =

func.func @testUnimplementedOp() -> (tensor<i32>, tensor<i32>) {
  %0 = arith.constant dense<1> : tensor<i32>
  %1 = arith.constant dense<2> : tensor<i32>
  %2 = "tf.Maximum"(%0, %1) {_output_shapes = ["tfshape$"]} : (tensor<i32>, tensor<i32>) -> tensor<i32>
  %3 = "tf.Minimum"(%0, %1) {random_attr = "hello"} : (tensor<i32>, tensor<i32>) -> tensor<i32>
  func.return %2, %3: tensor<i32>, tensor<i32>