def apply_final_layers(self, inputs):
    '剩余部分\n        '
    (c, x) = inputs
    z = self.layer_norm_conds[0]
    x = self.apply(inputs=self.simplify([x, z]), layer=LayerNormalization, center=False, epsilon=1e-06, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='Decoder-Output-Norm')
    x = self.apply(inputs=x, layer=Dropout, rate=self.dropout_rate, name='Decoder-Output-Dropout')
    x = self.apply(inputs=x, layer=Lambda, function=(lambda x: (x / np.sqrt(self.hidden_size))), mask=(lambda i, m: m), name='Decoder-Output-Scale')
    if self.with_lm:
        if (self.embedding_size != self.hidden_size):
            x = self.apply(inputs=x, layer=Dense, units=self.embedding_size, kernel_initializer=self.initializer, name='Decoder-Output-Mapping')
        lm_activation = ('softmax' if (self.with_lm is True) else self.with_lm)
        if (self.version == 't5.1.0'):
            x = self.apply(inputs=x, layer=Embedding, arguments={'mode': 'dense'}, name='Embedding-Token')
            x = self.apply(inputs=x, layer=Activation, activation=lm_activation, name='Dencoder-Output-LM-Activation')
        else:
            x = self.apply(inputs=x, layer=Dense, units=self.vocab_size, activation=lm_activation, use_bias=False, kernel_initializer=self.initializer, name='Decoder-Output-LM')
    return x
剩余部分
def apply_final_layers(self, inputs):
    '\n        '
    (c, x) = inputs
    z = self.layer_norm_conds[0]
    x = self.apply(inputs=self.simplify([x, z]), layer=LayerNormalization, center=False, epsilon=1e-06, conditional=(z is not None), hidden_units=self.layer_norm_conds[1], hidden_activation=self.layer_norm_conds[2], hidden_initializer=self.initializer, name='Decoder-Output-Norm')
    x = self.apply(inputs=x, layer=Dropout, rate=self.dropout_rate, name='Decoder-Output-Dropout')
    x = self.apply(inputs=x, layer=Lambda, function=(lambda x: (x / np.sqrt(self.hidden_size))), mask=(lambda i, m: m), name='Decoder-Output-Scale')
    if self.with_lm:
        if (self.embedding_size != self.hidden_size):
            x = self.apply(inputs=x, layer=Dense, units=self.embedding_size, kernel_initializer=self.initializer, name='Decoder-Output-Mapping')
        lm_activation = ('softmax' if (self.with_lm is True) else self.with_lm)
        if (self.version == 't5.1.0'):
            x = self.apply(inputs=x, layer=Embedding, arguments={'mode': 'dense'}, name='Embedding-Token')
            x = self.apply(inputs=x, layer=Activation, activation=lm_activation, name='Dencoder-Output-LM-Activation')
        else:
            x = self.apply(inputs=x, layer=Dense, units=self.vocab_size, activation=lm_activation, use_bias=False, kernel_initializer=self.initializer, name='Decoder-Output-LM')
    return x