code: def fully_connected(x, out_dim):
      """FullyConnected layer
  
      Parameters:
      x (Tensor): Input tensor to the fully connected layer
      out_dim (int): Output dimension of the fully connected layer.
  
      Return:
      The Tensor corresponding to the fully connected layer output.
      """
      w = tf.get_variable(
          "DW",
          [x.get_shape()[1], out_dim],
          initializer=tf.uniform_unit_scaling_initializer(factor=1.0),
      )
      b = tf.get_variable(
          "biases", [out_dim], initializer=tf.constant_initializer()
      )
      return tf.nn.xw_plus_b(x, w, b)
  
file_path: AIX360/aix360/algorithms/profwt/train_probes.py
imports: import time
  import six
  import sys
  import numpy
  import aix360.algorithms.profwt.resnet_keras_model
  import tensorflow
  from sklearn.utils import shuffle
  import json
label_desc: Use image node_id 1 for calling a global function with example usage: fully_connected(x, out_dim) and returns: tf