class_docstr: ConvNet model.
class_docstr_tok: ["ConvNet","model","."]
docstr: ConvNet constructor.
  
  Args:
    is_training: if build training or inference model.
    data_format: the data_format used during computation.
                 one of 'channels_first' or 'channels_last'.
docstr_tok: ["ConvNet","constructor",".","Args",":","is_training",":","if","build","training","or","inference","model",".","data_format",":","the","data_format","used","during","computation",".","one","of","'channels_first","'","or","'channels_last","'","."]
code: def __init__(
      self,
      is_training,
      data_format,
      batch_norm_decay,
      batch_norm_epsilon,
  ):
      """ConvNet constructor.
  
      Args:
        is_training: if build training or inference model.
        data_format: the data_format used during computation.
                     one of 'channels_first' or 'channels_last'.
      """
      self._batch_norm_decay = batch_norm_decay
      self._batch_norm_epsilon = batch_norm_epsilon
      self._is_training = is_training
      assert data_format in ("channels_first", "channels_last")
      self._data_format = data_format
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
label_desc: Use image node_id 1 to create a new ConvNet object from inherited base classes: object with example: obj = ConvNet(is_training, data_format, batch_norm_decay, batch_norm_epsilon)