class_docstr: Box collection.
  
  BoxList represents a list of bounding boxes as numpy array, where each
  bounding box is represented as a row of 4 numbers,
  [y_min, x_min, y_max, x_max].  It is assumed that all bounding boxes within a
  given list correspond to a single image.
  
  Optionally, users can add additional related fields (such as
  objectness/classification scores).
docstr: Check whether data fullfills the format of N*[ymin, xmin, ymax, xmin].
  
  Args:
    data: a numpy array of shape [N, 4] representing box coordinates
  
  Returns:
    a boolean indicating whether all ymax of boxes are equal or greater than
        ymin, and all xmax of boxes are equal or greater than xmin.
code: def _is_valid_boxes(self, data):
      """Check whether data fullfills the format of N*[ymin, xmin, ymax, xmin].
  
      Args:
        data: a numpy array of shape [N, 4] representing box coordinates
  
      Returns:
        a boolean indicating whether all ymax of boxes are equal or greater than
            ymin, and all xmax of boxes are equal or greater than xmin.
      """
      if data.shape[0] > 0:
          for i in range(data.shape[0]):
              if data[i, 0] > data[i, 2] or data[i, 1] > data[i, 3]:
                  return False
      return True
  
file_path: SlowFast/ava_evaluation/np_box_list.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import numpy
label_desc: Use image node_id 9 for calling the BoxList obj's underlying member method code with example usage: obj._is_valid_boxes(data) and returns: True, False