(inputs, is_training, bn_decay, scope)
return batch_norm_template(inputs, is_training, scope, [0, 1, 2, 3], bn_decay)
Batch normalization on 3D convolutional maps.
  
  Args:
      inputs:      Tensor, 5D BDHWC input maps
      is_training: boolean tf.Varialbe, true indicates training phase
      bn_decay:    float or float tensor variable, controling moving average weight
      scope:       string, variable scope
  Return:
      normed:      batch-normalized maps
Batch normalization on 3D convolutional maps.
  
  Args:
      inputs:      Tensor, 5D BDHWC input maps
      is_training: boolean tf.Varialbe, true indicates training phase
      bn_decay:    float or float tensor variable, controling moving average weight
      scope:       string, variable scope
  Return:
      normed:      batch-normalized maps
Batch normalization on 3D convolutional maps . Args : inputs : Tensor 5D BDHWC input maps is_training : boolean tf . Varialbe true indicates training phase bn_decay : float or float tensor variable controling moving average weight scope : string variable scope Return : normed : batch - normalized maps
def batch_norm_for_conv3d(inputs, is_training, bn_decay, scope):
    """ Batch normalization on 3D convolutional maps.
  
  Args:
      inputs:      Tensor, 5D BDHWC input maps
      is_training: boolean tf.Varialbe, true indicates training phase
      bn_decay:    float or float tensor variable, controling moving average weight
      scope:       string, variable scope
  Return:
      normed:      batch-normalized maps
  """
    return batch_norm_template(inputs, is_training, scope, [0, 1, 2, 3], bn_decay)
def batch_norm_for_conv3d ( inputs , is_training , bn_decay , scope ) : return batch_norm_template ( inputs , is_training , scope , [ 0 , 1 , 2 , 3 ] , bn_decay )