code: def __init__(
      self,
      dev,
      rows=28,
      cols=28,
      channels=1,
      noise_size=100,
      hidden_size=128,
      batch=128,
      interval=1000,
      learning_rate=0.001,
      iterations=1000000,
      dataset_filepath="mnist.pkl.gz",
      file_dir="vanilla_images/",
  ):
      self.dev = dev
      self.rows = rows
      self.cols = cols
      self.channels = channels
      self.feature_size = self.rows * self.cols * self.channels
      self.noise_size = noise_size
      self.hidden_size = hidden_size
      self.batch = batch
      self.batch_size = self.batch // 2
      self.interval = interval
      self.learning_rate = learning_rate
      self.iterations = iterations
      self.dataset_filepath = dataset_filepath
      self.file_dir = file_dir
      self.model = gan_mlp.create_model(
          noise_size=self.noise_size,
          feature_size=self.feature_size,
          hidden_size=self.hidden_size,
      )
  
file_path: singa/examples/gan/vanilla.py
imports: from singa import device
  from singa import opt
  from singa import tensor
  import argparse
  import matplotlib.pyplot
  import numpy
  import os
  from model import gan_mlp
  from utils import load_data
  from utils import print_log
label_desc: Use image node_id 1 to create a new VANILLA object  with example: obj = VANILLA(dev, rows, cols, channels, noise_size, hidden_size, batch, interval, learning_rate, iterations, dataset_filepath, file_dir)