src/transformers/models/sew/modeling_sew.py/SEWEncoder/create_custom_forward
class SEWEncoder:    def forward(
        self,
        hidden_states,
        attention_mask=None,
        output_attentions=False,
        output_hidden_states=False,
        return_dict=True,
    ):
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None

        if attention_mask is not None:
            # make sure padded tokens output 0
            hidden_states[~attention_mask] = 0.0

            input_lengths = (attention_mask.long()).sum(-1)
            # apply pooling formula to get real output_lengths
            output_lengths = input_lengths // self.config.squeeze_factor
            max_encoder_length = hidden_states.shape[1] // self.config.squeeze_factor
            attention_ids = (
                torch.arange(0, max_encoder_length, device=output_lengths.device)
                .view(1, -1)
                .expand(output_lengths.shape[0], -1)
            )
            attention_mask = (attention_ids < output_lengths.view(-1, 1)).long()

            # extend attention_mask
            attention_mask = 1.0 - attention_mask[:, None, None, :].to(dtype=hidden_states.dtype)
            attention_mask = attention_mask * torch.finfo(hidden_states.dtype).min
            attention_mask = attention_mask.expand(
                attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]
            )

        n_input_timesteps = hidden_states.shape[1]

        hidden_states = hidden_states.transpose(1, 2)
        position_embeddings = self.pos_conv_embed(hidden_states)
        pooled_hidden_states = self.pool(hidden_states)
        min_length = min(position_embeddings.size(-1), pooled_hidden_states.size(-1))
        hidden_states = pooled_hidden_states[..., :min_length] + position_embeddings[..., :min_length]
        hidden_states = hidden_states.transpose(1, 2)

        hidden_states = self.layer_norm(hidden_states)
        hidden_states = self.dropout(hidden_states)

        deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled()

        for layer in self.layers:
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
            dropout_probability = np.random.uniform(0, 1)

            skip_the_layer = True if self.training and (dropout_probability < self.config.layerdrop) else False
            if not skip_the_layer or deepspeed_zero3_is_enabled:
                # under deepspeed zero3 all gpus must run in sync
                if self.gradient_checkpointing and self.training:
                    # create gradient checkpointing function
                    def create_custom_forward(module):
                        def custom_forward(*inputs):
                            return module(*inputs, output_attentions)

                        return custom_forward

                    layer_outputs = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(layer),
                        hidden_states,
                        attention_mask,
                    )
                else:
                    layer_outputs = layer(
                        hidden_states, attention_mask=attention_mask, output_attentions=output_attentions
                    )
                hidden_states = layer_outputs[0]

            if skip_the_layer:
                layer_outputs = (None, None)

            if output_attentions:
                all_self_attentions = all_self_attentions + (layer_outputs[1],)

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        hidden_states = self.upsample(hidden_states)
        if hidden_states.shape[1] < n_input_timesteps:
            hidden_states = nn.functional.pad(hidden_states, (0, 0, 0, n_input_timesteps - hidden_states.shape[1]))

        if not return_dict:
            return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
        return BaseModelOutput(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
        )