code: def from_config(
      cls, config, meta: CommonMetadata = None, tensorizers=None
  ):
      # TODO: refactor metric reporting and remove this hack
      if tensorizers:
          labels = list(tensorizers["labels"].vocab)
      else:
          labels = meta.target.vocab.itos
          config.text_column_names = []
      return cls.from_config_and_label_names(config, labels)
  
imports: from enum import Enum
  from itertools import tee, zip_longest
  from typing import Generator, List, Optional, Sequence
  import torch
  from pytext.common.constants import Stage
  from pytext.data import CommonMetadata
  from pytext.metrics import ClassificationMetrics, compute_classification_metrics, compute_multi_label_classification_metrics, LabelListPrediction, LabelPrediction, LabelTopKPrediction, RECALL_AT_PRECISION_THRESHOLDS, safe_division
  from pytext.metrics.intent_slot_metrics import PerLabelConfusions
  from .channel import Channel, ConsoleChannel, FileChannel
  from .metric_reporter import MetricReporter
label_desc: Use image node_id 2 for calling the ClassificationMetricReporter obj's underlying member method code with example usage: obj.from_config(cls, config, meta, tensorizers) and returns: cls