def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        position_embeddings: torch.Tensor = None,
        reference_points=None,
        spatial_shapes=None,
        level_start_index=None,
        output_attentions: bool = False,
    ):
        """
        Args:
            hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
                Input to the layer.
            attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
                Attention mask.
            position_embeddings (`torch.FloatTensor`, *optional*):
                Position embeddings, to be added to `hidden_states`.
            reference_points (`torch.FloatTensor`, *optional*):
                Reference points.
            spatial_shapes (`torch.LongTensor`, *optional*):
                Spatial shapes of the backbone feature maps.
            level_start_index (`torch.LongTensor`, *optional*):
                Level start index.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
        """
        residual = hidden_states

        # Apply Multi-scale Deformable Attention Module on the multi-scale feature maps.
        hidden_states, attn_weights = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            encoder_hidden_states=hidden_states,
            encoder_attention_mask=attention_mask,
            position_embeddings=position_embeddings,
            reference_points=reference_points,
            spatial_shapes=spatial_shapes,
            level_start_index=level_start_index,
            output_attentions=output_attentions,
        )

        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)

        residual = hidden_states
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)

        hidden_states = self.fc2(hidden_states)
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)

        hidden_states = residual + hidden_states
        hidden_states = self.final_layer_norm(hidden_states)

        if self.training:
            if torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any():
                clamp_value = torch.finfo(hidden_states.dtype).max - 1000
                hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attn_weights,)

        return outputs

        Args:
            hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
                Input to the layer.
            attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
                Attention mask.
            position_embeddings (`torch.FloatTensor`, *optional*):
                Position embeddings, to be added to `hidden_states`.
            reference_points (`torch.FloatTensor`, *optional*):
                Reference points.
            spatial_shapes (`torch.LongTensor`, *optional*):
                Spatial shapes of the backbone feature maps.
            level_start_index (`torch.LongTensor`, *optional*):
                Level start index.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
        
dpkd/transformers/src/transformers/models/deta/modeling_deta.py