class_docstr: A Datasets-based preprocessor that fits scalers/encoders to the training
  dataset and transforms the training, testing, and inference datasets using
  those fitted scalers/encoders.
class_docstr_tok: ["A","Datasets-based","preprocessor","that","fits","scalers\/encoders","to","the","training","dataset","and","transforms","the","training",",","testing",",","and","inference","datasets","using","those","fitted","scalers\/encoders","."]
code: def __init__(self):
      # List of present fruits, used for one-hot encoding of fruit column.
      self.fruits = None
      # Mean and stddev stats used for standard scaling of the feature
      # columns.
      self.standard_stats = None
  
imports: import argparse
  import collections
  import os
  import sys
  import time
  from tempfile import TemporaryDirectory
  from typing import Tuple
  import boto3
  import mlflow
  import pandas
  import torch
  import torch.nn
  import torch.optim
  import ray
  from ray import train
  from ray.air.integrations.mlflow import MLflowLoggerCallback
  from ray.data.aggregate import Mean, Std
  from ray.train import Checkpoint, DataConfig, RunConfig, ScalingConfig
  from ray.train.torch.torch_trainer import TorchTrainer
label_desc: Use image node_id 1 to create a new DataPreprocessor object  with example: obj = DataPreprocessor()