def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        object_queries: Optional[torch.Tensor] = None,
        query_position_embeddings: Optional[torch.Tensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = False,
        **kwargs,
    ):
        """
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`): attention mask of size
                `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative
                values.
            object_queries (`torch.FloatTensor`, *optional*):
                object_queries that are added to the hidden states
            in the cross-attention layer.
            query_position_embeddings (`torch.FloatTensor`, *optional*):
                position embeddings that are added to the queries and keys
            in the self-attention layer.
            encoder_hidden_states (`torch.FloatTensor`):
                cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
            encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
                `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative
                values.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
        """
        position_embeddings = kwargs.pop("position_embeddings", None)

        if kwargs:
            raise ValueError(f"Unexpected arguments {kwargs.keys()}")

        if position_embeddings is not None and object_queries is not None:
            raise ValueError(
                "Cannot specify both position_embeddings and object_queries. Please use just object_queries"
            )

        if position_embeddings is not None:
            logger.warning_once(
                "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead"
            )
            object_queries = position_embeddings

        residual = hidden_states

        # Self Attention
        hidden_states, self_attn_weights = self.self_attn(
            hidden_states=hidden_states,
            object_queries=query_position_embeddings,
            attention_mask=attention_mask,
            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)

        # Cross-Attention Block
        cross_attn_weights = None
        if encoder_hidden_states is not None:
            residual = hidden_states

            hidden_states, cross_attn_weights = self.encoder_attn(
                hidden_states=hidden_states,
                object_queries=query_position_embeddings,
                key_value_states=encoder_hidden_states,
                attention_mask=encoder_attention_mask,
                spatial_position_embeddings=object_queries,
                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.encoder_attn_layer_norm(hidden_states)

        # Fully Connected
        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)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights, cross_attn_weights)

        return outputs

        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`): attention mask of size
                `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative
                values.
            object_queries (`torch.FloatTensor`, *optional*):
                object_queries that are added to the hidden states
            in the cross-attention layer.
            query_position_embeddings (`torch.FloatTensor`, *optional*):
                position embeddings that are added to the queries and keys
            in the self-attention layer.
            encoder_hidden_states (`torch.FloatTensor`):
                cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
            encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
                `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative
                values.
            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/maskformer/modeling_maskformer.py