docstr: Calculate the prediction of the data.
  Args:
      data(numpy.ndarray): input data with shape (size,
      height, width, channels).
  Return:
      numpy.ndarray: predictions of the data with shape (batch_size,
          num_of_classes).
docstr_tok: ["Calculate","the","prediction","of","the","data",".","Args",":","data","(","numpy.ndarray",")",":","input","data","with","shape","(","size",",","height",",","width",",","channels",")",".","Return",":","numpy.ndarray",":","predictions","of","the","data","with","shape","(","batch_size",",","num_of_classes",")","."]
code: def predict(self, data):
      """
      Calculate the prediction of the data.
      Args:
          data(numpy.ndarray): input data with shape (size,
          height, width, channels).
      Return:
          numpy.ndarray: predictions of the data with shape (batch_size,
              num_of_classes).
      """
  
      import tensorflow as tf
  
      scaled_data = self._process_input(data)
  
      # print(scaled_data)
  
      fd = {self._input: scaled_data}
  
      # Run prediction
      predict = self._session.run(self._logits, feed_dict=fd)
      predict = np.squeeze(predict, axis=0)
  
      return predict
  
file_path: AdvBox/adversarialbox/models/tensorflow.py
imports: from __future__ import absolute_import
  from builtins import str
  from builtins import range
  import numpy
  import os
  from .base import Model
  import logging
label_desc: Use image node_id 2 for calling the TensorflowModel obj's underlying member method code with example usage: obj.predict(data) and returns: predict