class_docstr: y = a * x + b
class_docstr_tok: ["y","=","a","*","x","+","b"]
code: def __init__(self, size=1000):
      X1 = torch.randn(size // 2, 50)
      X2 = torch.randn(size // 2, 50) + 1.5
      self.x = torch.cat([X1, X2], dim=0)
      Y1 = torch.zeros(size // 2, 1)
      Y2 = torch.ones(size // 2, 1)
      self.y = torch.cat([Y1, Y2], dim=0)
  
imports: import os
  from unittest import TestCase
  import numpy
  import pytest
  import torch
  import torch.nn
  from zoo.orca import OrcaContext
  from zoo.orca.data.pandas import read_csv
  from zoo.orca.learn.metrics import Accuracy
  from zoo import init_nncontext
  from zoo.orca.learn.pytorch import Estimator
  from zoo.orca.data import SparkXShards
  from zoo.orca.data.image.utils import chunks
label_desc: Use image node_id 1 to create a new LinearDataset object from inherited base classes: torch.utils.data with example: obj = LinearDataset(size)