code: def get_x_y_pairs(train, train_periods, prediction_periods):
      """
      train_scaled - training sequence
      train_periods - How many data points to use as inputs
      prediction_periods - How many periods to ouput as predictions
      """
      train_scaled = train.T
      r = train_scaled.shape[0] - train_periods - prediction_periods
      x_train = [train_scaled[i : i + train_periods] for i in range(r)]
      y_train = [
          train_scaled[
              i + train_periods : i + train_periods + prediction_periods
          ]
          for i in range(r)
      ]
  
      # -- use the stack function to convert the list of 1D tensors
      # into a 2D tensor where each element of the list is now a row
      x_train = np.concatenate(x_train, axis=0)
      y_train = np.concatenate(y_train, axis=0)
  
      return x_train, y_train
  
file_path: FEDOT/fedot/core/operations/evaluation/operation_implementations/data_operations/topological/hankel_matrix.py
imports: from typing import Union
  import numpy
  import pandas
  from scipy.linalg import hankel
label_desc: Use image node_id 1 for calling a global function with example usage: get_x_y_pairs(train, train_periods, prediction_periods) and returns: x_train,  y_train