code: def deepnn(x):
      """deepnn builds the graph for a deep net for classifying digits.
      Args:
          x: an input tensor with the dimensions (N_examples, 784), where 784 is
              the number of pixels in a standard MNIST image.
      Returns:
          A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with
              values equal to the logits of classifying the digit into one of 10
              classes (the digits 0-9). keep_prob is a scalar placeholder for the
              probability of dropout.
      """
      # Reshape to use within a convolutional neural net.
      # Last dimension is for "features" - there is only one here, since images
      # are grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
      with tf.name_scope("reshape"):
          x_image = tf.reshape(x, [-1, 28, 28, 1])
  
      # First convolutional layer - maps one grayscale image to 32 feature maps.
      with tf.name_scope("conv1"):
          W_conv1 = weight_variable([5, 5, 1, 32])
          b_conv1 = bias_variable([32])
          h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
  
      # Pooling layer - downsamples by 2X.
      with tf.name_scope("pool1"):
          h_pool1 = max_pool_2x2(h_conv1)
  
      # Second convolutional layer -- maps 32 feature maps to 64.
      with tf.name_scope("conv2"):
          W_conv2 = weight_variable([5, 5, 32, 64])
          b_conv2 = bias_variable([64])
          h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
  
      # Second pooling layer.
      with tf.name_scope("pool2"):
          h_pool2 = max_pool_2x2(h_conv2)
  
      # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
      # is down to 7x7x64 feature maps -- maps this to 1024 features.
      with tf.name_scope("fc1"):
          W_fc1 = weight_variable([7 * 7 * 64, 1024])
          b_fc1 = bias_variable([1024])
  
          h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
          h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
  
      # Dropout - controls the complexity of the model, prevents co-adaptation of
      # features.
      with tf.name_scope("dropout"):
          keep_prob = tf.placeholder(tf.float32)
          h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
  
      # Map the 1024 features to 10 classes, one for each digit
      with tf.name_scope("fc2"):
          W_fc2 = weight_variable([1024, 10])
          b_fc2 = bias_variable([10])
  
          y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
      return y_conv, keep_prob
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import time
  import tensorflow
  from tensorflow.examples.tutorials.mnist import input_data
  import ray
  import ray.experimental.tf_utils
  from bigdl.dllib.utils.log4Error import invalidInputError
label_desc: Use image node_id 2 for calling a global function with example usage: deepnn(x) and returns: y_conv,  keep_prob