code: def initial_preprocessor(data, json_file):
      object_columns = [
          col
          for col, col_type in data.dtypes.iteritems()
          if col_type == "object"
      ]
  
      # Handles dates without timestamps
      for col in object_columns:
          try:
              data[col] = pd.to_datetime(
                  data[col], infer_datetime_format=True
              )
          except ValueError:
              pass
  
      # get target column
      # preprocess the dataset
  
      data = {
          "train": pd.concat([data], axis=1),
      }
  
      data, full_pipeline = structured_preprocessor(data)
  
      return data
  
file_path: nylon/nylon/preprocessing/preprocessing_inference.py
imports: import numpy
  import pandas
  from sklearn.compose import ColumnTransformer
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.pipeline import Pipeline
  from prince.ca import CA
  from nltk.stem import WordNetLemmatizer
  from nltk.tokenize import word_tokenize
  from nltk.corpus import stopwords
  from autocorrect import Speller
  from sklearn.impute import SimpleImputer
  from sklearn.preprocessing import OneHotEncoder, StandardScaler, FunctionTransformer, LabelEncoder
label_desc: Use image node_id 1 for calling a global function with example usage: initial_preprocessor(data, json_file) and returns: data