class_docstr: Params for :py:class:`LogisticRegression` and :py:class:`LogisticRegressionModel`.
  
  .. versionadded:: 3.0.0
class_docstr_tok: ["Params","for",":","py",":","class",":","`","LogisticRegression","`","and",":","py",":","class",":","`","LogisticRegressionModel","`",".","..","versionadded",":",":","3.0.0"]
code: def __init__(self, *args: Any):
      super(_LogisticRegressionParams, self).__init__(*args)
      self._setDefault(
          maxIter=100,
          tol=1e-6,
          batchSize=32,
          learningRate=0.001,
          momentum=0.9,
          seed=0,
      )
  
imports: from typing import Any, Dict, Union, List, Tuple, Callable, Optional
  import math
  import torch
  import torch.nn
  import numpy
  import pandas
  from pyspark import keyword_only
  from pyspark.ml.connect.base import _PredictorParams
  from pyspark.ml.param.shared import HasProbabilityCol
  from pyspark.sql import DataFrame
  from pyspark.ml.common import inherit_doc
  from pyspark.ml.torch.distributor import TorchDistributor
  from pyspark.ml.param.shared import HasMaxIter, HasFitIntercept, HasTol, HasWeightCol, HasSeed, HasNumTrainWorkers, HasBatchSize, HasLearningRate, HasMomentum
  from pyspark.ml.connect.base import Predictor, PredictionModel
  from pyspark.ml.connect.io_utils import ParamsReadWrite, CoreModelReadWrite
  from pyspark.sql import functions
label_desc: Use image node_id 1 to create a new _LogisticRegressionParams object from inherited base classes: _PredictorParams, HasMaxIter, HasFitIntercept, HasTol, HasWeightCol, HasNumTrainWorkers, HasBatchSize, HasLearningRate, HasMomentum, HasProbabilityCol, HasSeed with example: obj = _LogisticRegressionParams()