$output_shapes, $use_inter_op_parallelism, $preserve_cardinality,
        $force_synchronous, $map_dataset_metadata),
     $batch_size, $drop_remainder, $parallel_copy, $batch_output_types,
     $batch_output_shapes, $unused_batch_dataset_metadata),
  (TF_MapAndBatchDatasetOp $input_dataset, $other_arguments, $batch_size,
     (TF_ConstOp (GetI64ScalarElementsAttr<1>)), $drop_remainder, $f,

  %5 = "tf.BatchDatasetV2"(%4, %0, %1) {device = "",
           output_shapes = [#tf_type.shape<>],
           output_types = [i32],
           parallel_copy = false,
           metadata = ""} : (tensor<!tf_type.variant>, tensor<i64>, tensor<i1>) -> tensor<!tf_type.variant>
  func.return %5 : tensor<!tf_type.variant>
}

  %6 = "tf.BatchDatasetV2"(%5, %0, %1) {device = "",
           output_shapes = [#tf_type.shape<>],
           output_types = [i32],
           parallel_copy = false,
           metadata = ""} : (tensor<!tf_type.variant>, tensor<i64>, tensor<i1>) -> tensor<!tf_type.variant>
  func.return %6 : tensor<!tf_type.variant>
}

          }
        }
      }
    }
  }
  attr {
    key: "output_types"
    value {
      list {
        type: DT_INT32
      }
    }
  }
  attr {
    key: "parallel_copy"
    value {
      b: false
    }
  }
}
library {
  function {
    signature {
      name: "__inference_Dataset_map_<lambda>_8"
      input_arg {
        name: "args_0"

    Arg<TF_BoolTensor, [{A scalar representing whether the last batch should be dropped in case its size
is smaller than desired.}]>:$drop_remainder,

    DefaultValuedOptionalAttr<BoolAttr, "false">:$parallel_copy,
    ConfinedAttr<TypeArrayAttr, [ArrayMinCount<1>]>:$output_types,
    ConfinedAttr<TF_ShapeAttrArray, [ArrayMinCount<1>]>:$output_shapes,
    DefaultValuedOptionalAttr<StrAttr, "\"\"">:$metadata
  );