block_zoo/attentions/MatchAttention.py [52:79]:
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        assert layer_conf.input_dims[0][-1] == layer_conf.input_dims[1][-1]
        self.linear = nn.Linear(layer_conf.input_dims[0][-1], layer_conf.input_dims[0][-1])
        if layer_conf.activation:
            self.activation = eval("nn." + self.layer_conf.activation)()
        else:
            self.activation = None
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x, x_len, y, y_len):
        """

        Args:
            x:      [batch_size, x_max_len, dim].
            x_len:  [batch_size], default is None.
            y:      [batch_size, y_max_len, dim].
            y_len:  [batch_size], default is None.

        Returns:
            output: has the same shape as x.

        """

        x_proj = self.linear(x)  # [batch_size, x_max_len, dim]
        y_proj = self.linear(y)  # [batch_size, y_max_len, dim]
        if self.activation:
            x_proj = self.activation(x_proj)
            y_proj = self.activation(y_proj)
        scores = x_proj.bmm(y_proj.transpose(2, 1))     # [batch_size, x_max_len, y_max_len]
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block_zoo/op/Match.py [51:78]:
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        assert layer_conf.input_dims[0][-1] == layer_conf.input_dims[1][-1]
        self.linear = nn.Linear(layer_conf.input_dims[0][-1], layer_conf.input_dims[0][-1])
        if layer_conf.activation:
            self.activation = eval("nn." + self.layer_conf.activation)()
        else:
            self.activation = None
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x, x_len, y, y_len):
        """

        Args:
            x:      [batch_size, x_max_len, dim].
            x_len:  [batch_size], default is None.
            y:      [batch_size, y_max_len, dim].
            y_len:  [batch_size], default is None.

        Returns:
            output: has the same shape as x.

        """

        x_proj = self.linear(x)  # [batch_size, x_max_len, dim]
        y_proj = self.linear(y)  # [batch_size, y_max_len, dim]
        if self.activation:
            x_proj = self.activation(x_proj)
            y_proj = self.activation(y_proj)
        scores = x_proj.bmm(y_proj.transpose(2, 1))     # [batch_size, x_max_len, y_max_len]
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