class_docstr: This dataset describes engine noise with reading of length 500 from an automotive subsystem.
  Train and test data sets were collected in typical operating conditions, with minimal noise
  contamination.
  
  Train dataset consists of 1361 time series readings and test dataset consists of
  1320 readings.
  
  The units are a count and there are 1361 observations. The source of the dataset is
  UCR donated by A. Bagnall.
  
  References:
      .. [#1] Hoang Anh Dau, Eamonn Keogh, Kaveh Kamgar, Chin-Chia Michael Yeh, Yan Zhu,
      Shaghayegh Gharghabi , Chotirat Ann Ratanamahatana, Yanping Chen, Bing Hu,
      Nurjahan Begum, Anthony Bagnall , Abdullah Mueen, Gustavo Batista, & Hexagon-ML (2019).
      The UCR Time Series Classification Archive.
      URL https://www.cs.ucr.edu/~eamonn/time_series_data_2018/.
      .. [#2] http://www.timeseriesclassification.com/description.php?Dataset=FordA
docstr: Prepares train and test dataframes.
  
  Returns:
  
      x_train (np.ndarray): Train data in numpy format.
      x_test (np.ndarray): Test data in numpy format.
      y_train (np.ndarray): Train labels in numpy format.
      x_test (np.ndarray): Test labels in numpy format.
docstr_tok: ["Prepares","train","and","test","dataframes",".","Returns",":","x_train","(","np.ndarray",")",":","Train","data","in","numpy","format",".","x_test","(","np.ndarray",")",":","Test","data","in","numpy","format",".","y_train","(","np.ndarray",")",":","Train","labels","in","numpy","format",".","x_test","(","np.ndarray",")",":","Test","labels","in","numpy","format","."]
code: def load_data(self):
      """
      Prepares train and test dataframes.
  
      Returns:
  
          x_train (np.ndarray): Train data in numpy format.
          x_test (np.ndarray): Test data in numpy format.
          y_train (np.ndarray): Train labels in numpy format.
          x_test (np.ndarray): Test labels in numpy format.
      """
      train = pd.read_csv(
          self.train_data_file, delim_whitespace=True, header=None
      )
      test = pd.read_csv(
          self.test_data_file, delim_whitespace=True, header=None
      )
  
      y_train = train.iloc[:, 0]
      x_train = train.iloc[:, 1:]
      x_test = test.iloc[:, 1:]
      y_test = test.iloc[:, 0]
  
      x_train = x_train.values.reshape(-1, self.input_length, 1)
      x_test = x_test.values.reshape(-1, self.input_length, 1)
  
      y_train[y_train == -1] = 0
      y_test[y_test == -1] = 0
  
      return x_train, x_test, y_train, y_test
  
imports: import os
  import requests
  import pandas
  from zipfile import ZipFile
  from io import BytesIO
label_desc: Use image node_id 2 for calling the FordDataset obj's underlying member method code with example usage: obj.load_data() and returns: x_train,  x_test,  y_train,  y_test