def extract_features(self, inputs):
        """use convolution layer to extract feature .

        Args:
            inputs (tensor): Input tensor.

        Returns:
            Output of the final convolution
            layer in the efficientnet model.
        """
        # Stem
        x = self._swish(self._bn0(self._conv_stem(inputs)))

        # Blocks
        for idx, block in enumerate(self._blocks):
            drop_connect_rate = self._global_params.drop_connect_rate
            if drop_connect_rate:
                drop_connect_rate *= float(idx) / len(self._blocks)  # scale drop connect_rate
            x = block(x, drop_connect_rate=drop_connect_rate)

        # Head
        x = self._swish(self._bn1(self._conv_head(x)))

        return x
use convolution layer to extract feature .

        Args:
            inputs (tensor): Input tensor.

        Returns:
            Output of the final convolution
            layer in the efficientnet model.
        
efficientnet_pytorch/model.py