code: def InitializeWeightsBiases(
      prev_layer_size, size, weights=None, biases=None, name=None
  ):
      """Initializes weights and biases to be used in a fully-connected layer.
  
      Parameters
      ----------
      prev_layer_size: int
        Number of features in previous layer.
      size: int
        Number of nodes in this layer.
      weights: tf.Tensor, optional (Default None)
        Weight tensor.
      biases: tf.Tensor, optional (Default None)
        Bias tensor.
      name: str
        Name for this op, optional (Defaults to 'fully_connected' if None)
  
      Returns
      -------
      weights: tf.Variable
        Initialized weights.
      biases: tf.Variable
        Initialized biases.
  
      """
  
      if weights is None:
          weights = tf.truncated_normal(
              [prev_layer_size, size], stddev=0.01
          )
      if biases is None:
          biases = tf.zeros([size])
  
      with tf.name_scope(name, "fully_connected", [weights, biases]):
          w = tf.Variable(weights, name="w")
          b = tf.Variable(biases, name="b")
      return w, b
  
file_path: deepchem/contrib/atomicconv/models/atomicnet_ops.py
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import unicode_literals
  import numpy
  import tensorflow
label_desc: Use image node_id 1 for calling a global function with example usage: InitializeWeightsBiases(prev_layer_size, size, weights, biases, name) and returns: w,  b