class_docstr: Takes care of preprocessing for static_features and past/known covariates.
  
  Covariates are all converted to float dtype. Static features, if present, are all converted to categorical & float
  dtypes.
class_docstr_tok: ["Takes","care","of","preprocessing","for","static_features","and","past\/known","covariates",".","Covariates","are","all","converted","to","float","dtype",".","Static","features",",","if","present",",","are","all","converted","to","categorical","&","float","dtypes","."]
docstr: In-place convert the dtype of all numerical (float or int) columns to the given float dtype.
docstr_tok: ["In-place","convert","the","dtype","of","all","numerical","(","float","or","int",")","columns","to","the","given","float","dtype","."]
code: def _convert_numerical_features_to_float(
      df: pd.DataFrame, float_dtype=np.float64
  ) -> pd.DataFrame:
      """In-place convert the dtype of all numerical (float or int) columns to the given float dtype."""
      numeric_columns = [
          col
          for col in df.columns
          if pd.api.types.is_numeric_dtype(df[col])
      ]
      df[numeric_columns] = df[numeric_columns].astype(float_dtype)
      return df
  
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 3 for calling the TimeSeriesFeatureGenerator obj's underlying member method code with example usage: obj._convert_numerical_features_to_float(df, float_dtype) and returns: df