code: def __init__(
      self,
      channels,
      metadata,
      tensorizers,
      aggregate_metrics,
      perplexity_type,
      pep_format,
      log_gradient=False,
  ):
      super().__init__(
          channels, log_gradient=log_gradient, pep_format=pep_format
      )
      self.metadata = metadata
      self.tensorizers = tensorizers
      self.aggregate_metrics = aggregate_metrics
  
      assert metadata or tensorizers
      if metadata:
          self.pad_index = metadata.target.pad_token_idx
      if tensorizers:
          if self.TOKENS_COLUMN in tensorizers:
              column = self.TOKENS_COLUMN
          elif self.LABELS_COLUMN in tensorizers:
              column = self.LABELS_COLUMN
          if hasattr(tensorizers[column], "vocab"):
              self.pad_index = tensorizers[column].vocab.get_pad_index()
          else:
              self.pad_index = tensorizers[column].PAD_BYTE
      self.perplexity_func = get_perplexity_func(perplexity_type)
  
imports: import json
  import math
  import operator
  import time
  import torch
  import torch.nn.functional
  from pytext.common.constants import Stage
  from pytext.config.module_config import PerplexityType
  from pytext.data import CommonMetadata
  from pytext.metrics.language_model_metrics import compute_language_model_metric, LanguageModelMetric
  from pytext.utils import cuda
  from .channel import ConsoleChannel, FileChannel
  from .metric_reporter import MetricReporter
label_desc: Use image node_id 2 to create a new LanguageModelMetricReporter object from inherited base classes: MetricReporter with example: obj = LanguageModelMetricReporter(channels, metadata, tensorizers, aggregate_metrics, perplexity_type, pep_format, log_gradient)