parameters: (self, value)
docstring: Update the mean and variance estimates.
  
      Args:
        value: Batch or single value tensor.
  
      Returns:
        Summary tensor.
docstring_summary: Update the mean and variance estimates.
docstring_tokens: Update the mean and variance estimates .
function: def update(self, value):
      """Update the mean and variance estimates.
  
      Args:
        value: Batch or single value tensor.
  
      Returns:
        Summary tensor.
      """
      with tf.name_scope(self._name + '/update'):
        if value.shape.ndims == self._mean.shape.ndims:
          # Add a batch dimension if necessary.
          value = value[None, ...]
        count = tf.shape(value)[0]
        with tf.control_dependencies([self._count.assign_add(count)]):
          step = tf.cast(self._count, tf.float32)
          mean_delta = tf.reduce_sum(value - self._mean[None, ...], 0)
          new_mean = self._mean + mean_delta / step
          new_mean = tf.cond(self._count > 1, lambda: new_mean, lambda: value[0])
          var_delta = (value - self._mean[None, ...]) * (value - new_mean[None, ...])
          new_var_sum = self._var_sum + tf.reduce_sum(var_delta, 0)
        with tf.control_dependencies([new_mean, new_var_sum]):
          update = self._mean.assign(new_mean), self._var_sum.assign(new_var_sum)
        with tf.control_dependencies(update):
          if value.shape.ndims == 1:
            value = tf.reduce_mean(value)
          return self._summary('value', tf.reduce_mean(value))
function_tokens: def update ( self , value ) : with tf . name_scope ( self . _name + '/update' ) : if value . shape . ndims == self . _mean . shape . ndims : # Add a batch dimension if necessary. value = value [ None , ... ] count = tf . shape ( value ) [ 0 ] with tf . control_dependencies ( [ self . _count . assign_add ( count ) ] ) : step = tf . cast ( self . _count , tf . float32 ) mean_delta = tf . reduce_sum ( value - self . _mean [ None , ... ] , 0 ) new_mean = self . _mean + mean_delta / step new_mean = tf . cond ( self . _count > 1 , lambda : new_mean , lambda : value [ 0 ] ) var_delta = ( value - self . _mean [ None , ... ] ) * ( value - new_mean [ None , ... ] ) new_var_sum = self . _var_sum + tf . reduce_sum ( var_delta , 0 ) with tf . control_dependencies ( [ new_mean , new_var_sum ] ) : update = self . _mean . assign ( new_mean ) , self . _var_sum . assign ( new_var_sum ) with tf . control_dependencies ( update ) : if value . shape . ndims == 1 : value = tf . reduce_mean ( value ) return self . _summary ( 'value' , tf . reduce_mean ( value ) )