class_docstr: Generates categorical and continuous features for time series models.
class_docstr_tok: ["Generates","categorical","and","continuous","features","for","time","series","models","."]
code: def __init__(self, verbosity: int = 0, **kwargs):
      generators = [
          CategoryFeatureGenerator(minimum_cat_count=1, fillna="mode"),
          IdentityFeatureGenerator(
              infer_features_in_args={
                  "valid_raw_types": [R_INT, R_FLOAT]
              }
          ),
      ]
      super().__init__(
          generators=[generators],
          post_generators=[],
          pre_generators=[AsTypeFeatureGenerator(convert_bool=False)],
          pre_enforce_types=False,
          pre_drop_useless=False,
          verbosity=verbosity,
          **kwargs,
      )
  
imports: import logging
  import reprlib
  from dataclasses import dataclass, field
  from typing import List, Optional
  import numpy
  import pandas
  from autogluon.common.features.types import R_FLOAT, R_INT
  from autogluon.features.generators import AsTypeFeatureGenerator, CategoryFeatureGenerator, IdentityFeatureGenerator, PipelineFeatureGenerator
  from autogluon.timeseries import TimeSeriesDataFrame
label_desc: Use image node_id 1 to create a new ContinuousAndCategoricalFeatureGenerator object from inherited base classes: PipelineFeatureGenerator with example: obj = ContinuousAndCategoricalFeatureGenerator(verbosity)