class_docstr: VowpalWabbit Model: https://vowpalwabbit.org/
  
  VowpalWabbit Command Line args: https://github.com/VowpalWabbit/vowpal_wabbit/wiki/Command-line-arguments
class_docstr_tok: ["VowpalWabbit","Model",":","https",":","\/\/vowpalwabbit.org\/","VowpalWabbit","Command","Line","args",":","https",":","\/\/github.com\/VowpalWabbit\/vowpal_wabbit\/wiki\/Command-line-arguments"]
code: def _fit(
      self,
      X: pd.DataFrame,
      y: pd.Series,
      time_limit=None,
      verbosity=2,
      **kwargs,  # training data  # training labels
  ):  # kwargs includes many other potential inputs, refer to AbstractModel documentation for details
      time_start = time.time()
      try_import_vowpalwabbit()
      import vowpalwabbit
  
      seed = 0  # Random seed
  
      # Valid self.problem_type values include ['binary', 'multiclass', 'regression', 'quantile', 'softclass']
      if (
          self.problem_type
          not in PROBLEM_TYPES_REGRESSION + PROBLEM_TYPES_CLASSIFICATION
      ):
          raise TypeError(
              f"Vowpal Wabbit does not support {self.problem_type}"
          )
  
      # Certain parameters like passes are passed as hyperparameters but are not used
      # while initialising the model.
      # passes: Used as epochs
  
      params = self._get_model_params()
      params["loss_function"] = params.get(
          "loss_function", self._get_default_loss_function()
      )
      passes = params.pop("passes")
  
      # Make sure to call preprocess on X near the start of `_fit`.
      # This is necessary because the data is converted via preprocess during predict, and needs to be in the same format as during fit.
      X_series = self.preprocess(X, is_train=True)
  
      self._validate_loss_function(
          loss_function=params["loss_function"]
      )
  
      # VW expects label from 1 to N for Binary and Multiclass classification problems
      # AutoGluon does label encoding from 0 to N-1, hence we increment the value of y by 1
      if self.problem_type != REGRESSION:
          y = y.apply(lambda row: row + 1)
      y = y.astype(str) + " "
  
      # Concatenate y and X to get the training data in VW format
      final_training_data = y + X_series
      final_training_data = final_training_data.tolist()
  
      extra_params = {
          "cache_file": "train.cache",
          "holdout_off": True,
      }
  
      if verbosity <= 3:
          extra_params["quiet"] = True
  
      # Initialize the model
      if self.problem_type in PROBLEM_TYPES_CLASSIFICATION:
          # Ref: https://github.com/VowpalWabbit/vowpal_wabbit/wiki/Predicting-probabilities#multi-class---oaa
          extra_params["oaa"] = self.num_classes
          extra_params["probabilities"] = True
      self.model = vowpalwabbit.Workspace(**params, **extra_params)
  
      time_start_fit = time.time()
      if time_limit is not None:
          time_limit_fit = (
              time_limit - (time_start_fit - time_start) - 0.3
          )  # Account for 0.3s overhead
          if time_limit_fit <= 0:
              raise TimeLimitExceeded
      else:
          time_limit_fit = None
  
      # Train the model
      np.random.seed(seed)
      epoch = 0
  
      for epoch in range(1, passes + 1):
          # TODO: Add Early Stopping support via validation
          self._train_single_epoch(training_data=final_training_data)
          if time_limit_fit is not None and epoch < passes:
              time_fit_used = time.time() - time_start_fit
              time_fit_used_per_epoch = time_fit_used / epoch
              time_left = time_limit_fit - time_fit_used
              if time_left <= (time_fit_used_per_epoch * 2):
                  logger.log(
                      30,
                      f"\tEarly stopping due to lack of time. Fit {epoch}/{passes} passes...",
                  )
                  break
  
      self.params_trained["passes"] = epoch
  
imports: import logging
  import os
  import time
  import numpy
  import pandas
  from autogluon.common.features.types import R_CATEGORY, R_FLOAT, R_INT, R_OBJECT, S_IMAGE_PATH, S_TEXT_AS_CATEGORY, S_TEXT_NGRAM, S_TEXT_SPECIAL
  from autogluon.common.utils.try_import import try_import_vowpalwabbit
  from autogluon.core.constants import BINARY, MULTICLASS, PROBLEM_TYPES_CLASSIFICATION, PROBLEM_TYPES_REGRESSION, REGRESSION
  from autogluon.core.models import AbstractModel
  from autogluon.core.utils.exceptions import TimeLimitExceeded
  from .vowpalwabbit_utils import VWFeaturesConverter
label_desc: Use image node_id 3 for calling the VowpalWabbitModel obj's underlying member method code with example usage: obj._fit(X, y, time_limit, verbosity) without return types