def call(self, inputs: tf.Tensor, training: bool = False,**kwargs):
        """Computes the Leaf representation of a batch of waveforms.

        Args:
          inputs: input audio of shape (batch_size, num_samples) or (batch_size,
            num_samples, 1).
          training: training mode, controls whether SpecAugment is applied or not.

        Returns:
          Leaf features of shape (batch_size, time_frames, freq_bins).
        """
        # Inputs should be [B, W] or [B, W, C]
        outputs = inputs[:, :, tf.newaxis] if inputs.shape.ndims < 3 else inputs
        if self._preemp:
            outputs = self._preemp_conv(outputs)
        outputs = self._complex_conv(outputs)
        outputs = self._activation(outputs)
        outputs = self._pooling(outputs)
        outputs = tf.maximum(outputs, 1e-5)
        outputs = self._compress_fn(outputs)
        if self._instance_norm is not None:
            outputs = self._instance_norm(outputs)
        if training:
            outputs = self._spec_augment_fn(outputs)

        return outputs[:,:,:,tf.newaxis]
Computes the Leaf representation of a batch of waveforms.

        Args:
          inputs: input audio of shape (batch_size, num_samples) or (batch_size,
            num_samples, 1).
          training: training mode, controls whether SpecAugment is applied or not.

        Returns:
          Leaf features of shape (batch_size, time_frames, freq_bins).
        
leaf_audio/frontend.py