class_docstr: MnistNetwork is for initializing and building basic network for mnist.
class_docstr_tok: ["MnistNetwork","is","for","initializing","and","building","basic","network","for","mnist","."]
code: def __init__(
      self,
      channel_1_num,
      channel_2_num,
      conv_size,
      hidden_size,
      pool_size,
      learning_rate,
      x_dim=784,
      y_dim=10,
  ):
      self.channel_1_num = channel_1_num
      self.channel_2_num = channel_2_num
      self.conv_size = conv_size
      self.hidden_size = hidden_size
      self.pool_size = pool_size
      self.learning_rate = learning_rate
      self.x_dim = x_dim
      self.y_dim = y_dim
  
      self.images = tf.placeholder(
          tf.float32, [None, self.x_dim], name="input_x"
      )
      self.labels = tf.placeholder(
          tf.float32, [None, self.y_dim], name="input_y"
      )
      self.keep_prob = tf.placeholder(tf.float32, name="keep_prob")
  
      self.train_step = None
      self.accuracy = None
  
file_path: nni/examples/trials/mnist-tfv1/mnist_before.py
imports: import argparse
  import logging
  import math
  import tempfile
  import time
  import tensorflow
  from tensorflow.examples.tutorials.mnist import input_data
label_desc: Use image node_id 1 to create a new MnistNetwork object from inherited base classes: object with example: obj = MnistNetwork(channel_1_num, channel_2_num, conv_size, hidden_size, pool_size, learning_rate, x_dim, y_dim)