code: def __init__(
      self,
      channels,
      log_gradient,
      tensorizers,
      accept_flat_intents_slots,
      model_select_metric_key,
      select_length_beam,
      ref_frame_accuracy,
      ref_model_num_param,
      param_importance,
  ):
      super().__init__(
          channels,
          log_gradient,
          tensorizers,
          accept_flat_intents_slots,
          model_select_metric_key,
          select_length_beam,
      )
  
      self.ref_frame_accuracy = ref_frame_accuracy
      self.ref_model_num_param = ref_model_num_param
      self.param_importance = param_importance
  
imports: from collections import defaultdict
  from typing import Dict, List
  import numpy
  from pytext.common.constants import Stage
  from pytext.data.tensorizers import Tensorizer
  from pytext.metric_reporters.channel import ConsoleChannel
  from pytext.metric_reporters.compositional_metric_reporter import CompositionalMetricReporter
  from pytext.metric_reporters.seq2seq_compositional import CompositionalSeq2SeqFileChannel, Seq2SeqCompositionalMetricReporter
  from pytext.metrics.intent_slot_metrics import FramePredictionPair
  from pytext.metrics.mask_metrics import compute_masked_metrics, compute_nas_masked_metrics
  from .compositional_utils import extract_beam_subtrees, filter_invalid_beams
label_desc: Use image node_id 1 to create a new NASMaskedSeq2SeqCompositionalMetricReporter object from inherited base classes: MaskedSeq2SeqCompositionalMetricReporter with example: obj = NASMaskedSeq2SeqCompositionalMetricReporter(channels, log_gradient, tensorizers, accept_flat_intents_slots, model_select_metric_key, select_length_beam, ref_frame_accuracy, ref_model_num_param, param_importance)