src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py/TFConv1dSubsampler/call
class TFConv1dSubsampler:    def call(self, input_features: tf.Tensor) -> tf.Tensor:
        # TF Conv1D assumes Batch x Time x Channels, same as the input
        hidden_states = tf.cast(input_features, tf.float32)
        for i, conv in enumerate(self.conv_layers):
            # equivalent to `padding=k // 2` on PT's `nn.Conv1d`
            pad_len = self.kernel_sizes[i] // 2
            hidden_shapes = shape_list(hidden_states)
            hidden_states = tf.concat(
                (
                    tf.zeros((hidden_shapes[0], pad_len, hidden_shapes[2])),
                    hidden_states,
                    tf.zeros((hidden_shapes[0], pad_len, hidden_shapes[2])),
                ),
                axis=1,
            )

            hidden_states = conv(hidden_states)
            hidden_states = glu(hidden_states, axis=2)  # GLU over the Channel dimension
        return hidden_states