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 mxnet
      import mxnet as mx
  
      scaled_data = self._process_input(data)
  
      scaled_data = mx.nd.array(scaled_data)
  
      # Run prediction
      predict = self._model(scaled_data).asnumpy()
      predict = np.squeeze(predict, axis=0)
  
      # logging.info(predict)
  
      return predict
  
file_path: AdvBox/adversarialbox/models/mxnet.py
imports: from __future__ import absolute_import
  import numpy
  import os
  from .base import Model
  import logging
label_desc: Use image node_id 2 for calling the MxNetModel obj's underlying member method code with example usage: obj.predict(data) and returns: predict