old_contents: import pandas as pd
  
  from .. import Interpreter as VanillaInterpreter, Time
  from ..errors import CraftAiNullDecisionError
  from .utils import is_valid_property_value, create_timezone_df, DUMMY_COLUMN_NAME, format_input
  
  def decide_from_row(tree, row, tz_col):
    context = {
      index: format_input(value) for index, value in row.iteritems()
      if is_valid_property_value(index, value)
    }
    time = Time(
      t=row.name.value // 1_000_000_000, # Timestamp.value returns nanoseconds
      timezone=row[tz_col] if tz_col else row.name.tz
    )
    try:
      decision = VanillaInterpreter.decide(tree, [context, time])
  
      return {
        f"{output}_{key}": value
        for output, output_decision in decision["output"].items()
        for key, value in output_decision.items()
      }
    except CraftAiNullDecisionError as e:
      return {"error": e.message}
  
  class Interpreter(VanillaInterpreter):
    @staticmethod
    def decide_from_contexts_df(tree, contexts_df):
      _, configuration, _ = VanillaInterpreter._parse_tree(tree)
      df = contexts_df.copy(deep=True)
  
      tz_col = [key for key, value in configuration["context"].items()
                if value["type"] == "timezone"]
      if tz_col:
        tz_col = tz_col[0]
      # If a timezone is needed create a timezone dataframe which will
      # store the timezone to use. It can either be the DatetimeIndex
      # timezone or the timezone column if provided.
      if tz_col:
        df[tz_col] = create_timezone_df(df, tz_col).iloc[:, 0]
  
      l_predictions = []
      for _, row in df.iterrows():
        prediction = decide_from_row(tree, row, tz_col)
        l_predictions.append(prediction)
      predictions_df = pd.DataFrame(l_predictions, index=df.index)
  
      return predictions_df
  
new_contents: import pandas as pd
  
  from .. import Interpreter as VanillaInterpreter, Time
  from ..errors import CraftAiNullDecisionError
  from .utils import is_valid_property_value, create_timezone_df, format_input
  
  def decide_from_row(tree, row, tz_col):
    context = {
      index: format_input(value) for index, value in row.iteritems()
      if is_valid_property_value(index, value)
    }
    time = Time(
      t=row.name.value // 1000000000, # Timestamp.value returns nanoseconds
      timezone=row[tz_col] if tz_col else row.name.tz
    )
    try:
      decision = VanillaInterpreter.decide(tree, [context, time])
  
      return {
        f"{output}_{key}": value
        for output, output_decision in decision["output"].items()
        for key, value in output_decision.items()
      }
    except CraftAiNullDecisionError as e:
      return {"error": e.message}
  
  class Interpreter(VanillaInterpreter):
    @staticmethod
    def decide_from_contexts_df(tree, contexts_df):
      _, configuration, _ = VanillaInterpreter._parse_tree(tree)
      df = contexts_df.copy(deep=True)
  
      tz_col = [key for key, value in configuration["context"].items()
                if value["type"] == "timezone"]
      if tz_col:
        tz_col = tz_col[0]
      # If a timezone is needed create a timezone dataframe which will
      # store the timezone to use. It can either be the DatetimeIndex
      # timezone or the timezone column if provided.
      if tz_col:
        df[tz_col] = create_timezone_df(df, tz_col).iloc[:, 0]
  
      i_predictions = (decide_from_row(tree, row, tz_col) for _, row in df.iterrows())
      predictions_df = pd.DataFrame(i_predictions, index=df.index)
  
      return predictions_df
  
subject: change a loop into a list comprehension, linting fix