parameters: (self, value)
docstring: Normalize a single or batch tensor.
  
      Applies the activated transformations in the constructor using current
      estimates of mean and variance.
  
      Args:
        value: Batch or single value tensor.
  
      Returns:
        Normalized batch or single value tensor.
docstring_summary: Normalize a single or batch tensor.
docstring_tokens: Normalize a single or batch tensor .
function: def transform(self, value):
      """Normalize a single or batch tensor.
  
      Applies the activated transformations in the constructor using current
      estimates of mean and variance.
  
      Args:
        value: Batch or single value tensor.
  
      Returns:
        Normalized batch or single value tensor.
      """
      with tf.name_scope(self._name + '/transform'):
        no_batch_dim = value.shape.ndims == self._mean.shape.ndims
        if no_batch_dim:
          # Add a batch dimension if necessary.
          value = value[None, ...]
        if self._center:
          value -= self._mean[None, ...]
        if self._scale:
          # We cannot scale before seeing at least two samples.
          value /= tf.cond(
              self._count > 1, lambda: self._std() + 1e-8, lambda: tf.ones_like(self._var_sum))[None]
        if self._clip:
          value = tf.clip_by_value(value, -self._clip, self._clip)
        # Remove batch dimension if necessary.
        if no_batch_dim:
          value = value[0]
        return tf.check_numerics(value, 'value')
function_tokens: def transform ( self , value ) : with tf . name_scope ( self . _name + '/transform' ) : no_batch_dim = value . shape . ndims == self . _mean . shape . ndims if no_batch_dim : # Add a batch dimension if necessary. value = value [ None , ... ] if self . _center : value -= self . _mean [ None , ... ] if self . _scale : # We cannot scale before seeing at least two samples. value /= tf . cond ( self . _count > 1 , lambda : self . _std ( ) + 1e-8 , lambda : tf . ones_like ( self . _var_sum ) ) [ None ] if self . _clip : value = tf . clip_by_value ( value , - self . _clip , self . _clip ) # Remove batch dimension if necessary. if no_batch_dim : value = value [ 0 ] return tf . check_numerics ( value , 'value' )