class_docstr: Pipeline feature generator with simplified arguments to handle most Tabular data including text and dates adequately.
  This is the default feature generation pipeline used by AutoGluon when unspecified.
  For more customization options, refer to :class:`PipelineFeatureGenerator` and :class:`BulkFeatureGenerator`.
  
  Parameters
  ----------
  enable_numeric_features : bool, default True
      Whether to keep features of 'int' and 'float' raw types.
      These features are passed without alteration to the models.
      Appends IdentityFeatureGenerator(infer_features_in_args=dict(valid_raw_types=['int', 'float']))) to the generator group.
  enable_categorical_features : bool, default True
      Whether to keep features of 'object' and 'category' raw types.
      These features are processed into memory optimized 'category' features.
      Appends CategoryFeatureGenerator() to the generator group.
  enable_datetime_features : bool, default True
      Whether to keep features of 'datetime' raw type and 'object' features identified as 'datetime_as_object' features.
      These features will be converted to 'int' features representing milliseconds since epoch.
      Appends DatetimeFeatureGenerator() to the generator group.
  enable_text_special_features : bool, default True
      Whether to use 'object' features identified as 'text' features to generate 'text_special' features such as word count,
      capital letter ratio, and symbol counts.
      Appends TextSpecialFeatureGenerator() to the generator group.
  enable_text_ngram_features : bool, default True
      Whether to use 'object' features identified as 'text' features to generate 'text_ngram' features.
      Appends TextNgramFeatureGenerator(vectorizer=vectorizer, text_ngram_params) to the generator group. See text_ngram.py for valid parameters.
  enable_raw_text_features : bool, default False
      Whether to use the raw text features. The generated raw text features will end up with '_raw_text' suffix.
      For example, 'sentence' --> 'sentence_raw_text'
  enable_vision_features : bool, default True
      [Experimental]
      Whether to keep 'object' features identified as 'image_path' special type.
      Features of this form should have a string path to an image file as their value.
      Only vision models can leverage these features, and these features will not be treated as categorical.
      Note: 'image_path' features will not be automatically inferred. These features must be explicitly specified as such in a custom FeatureMetadata object.
      Note: It is recommended that the string paths use absolute paths rather than relative, as it will likely be more stable.
  vectorizer : :class:`sklearn.feature_extraction.text.CountVectorizer`, default CountVectorizer(min_df=30, ngram_range=(1, 3), max_features=10000, dtype=np.uint8)  # noqa
      sklearn CountVectorizer object to use in :class:`TextNgramFeatureGenerator`.
      Only used if `enable_text_ngram_features=True`.
  **kwargs :
      Refer to :class:`AbstractFeatureGenerator` documentation for details on valid key word arguments.
  
  Examples
  --------
  >>> from autogluon.tabular import TabularDataset
  >>> from autogluon.features.generators import AutoMLPipelineFeatureGenerator
  >>>
  >>> feature_generator = AutoMLPipelineFeatureGenerator()
  >>>
  >>> label = 'class'
  >>> train_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv')
  >>> X_train = train_data.drop(labels=[label], axis=1)
  >>> y_train = train_data[label]
  >>>
  >>> X_train_transformed = feature_generator.fit_transform(X=X_train, y=y_train)
  >>>
  >>> test_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv')
  >>>
  >>> X_test_transformed = feature_generator.transform(test_data)
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code: def _get_category_feature_generator(self):
      return CategoryFeatureGenerator()
  
file_path: autogluon/features/src/autogluon/features/generators/auto_ml_pipeline.py
imports: import logging
  from autogluon.common.features.types import R_FLOAT, R_INT, R_OBJECT, S_IMAGE_BYTEARRAY, S_IMAGE_PATH, S_TEXT
  from .category import CategoryFeatureGenerator
  from .datetime import DatetimeFeatureGenerator
  from .identity import IdentityFeatureGenerator
  from .isnan import IsNanFeatureGenerator
  from .one_hot_encoder import OneHotEncoderFeatureGenerator
  from .pipeline import PipelineFeatureGenerator
  from .text_ngram import TextNgramFeatureGenerator
  from .text_special import TextSpecialFeatureGenerator
label_desc: Use image node_id 3 for calling the AutoMLPipelineFeatureGenerator obj's underlying member method code with example usage: obj._get_category_feature_generator() and returns: CategoryFeatureGenerator