Project: s41467-024-49516-2 | Function: forward (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Implements the forward pass of the Vector-Scalar Interactive Graph Neural Network (ViSNet) block, which is the core architecture of DeepGlycanSite. Processes protein graphs with geometric and evolutionary features through equivariant message passing layers with transformer attention mechanisms.

**KEY TERMS:** {
  "x": "node features h_n representing protein residues",
  "edge_attr": "edge features h_e capturing geometric relationships",
  "edge_vec": "directional edge vectors normalized and sphericalized",
  "attention_layers": "vector-scalar interactive updating units with transformer architecture",
  "distance_expansion": "radial basis function expansion g(r_ij)"
}

**CONTEXT CODE:**             cutoff_lower, cutoff_upper, num_rbf, trainable_rbf
        )

        self.rbf_proj = nn.Linear(num_rbf, hidden_channels)

        if self.x_dimension > 0:
            self.atom_embedding = IntEmbedding(self.x_dimension, hidden_channels, usage='x')
        else:
            raise ValueError('atom_feature must be specified')

        if self.edge_dimension > 0:
            self.bond_embedding = IntEmbedding(self.edge_dimension, hidden_channels, usage='edge')
        else:
            self.bond_embedding = None
        
        self.neighbor_embedding = NeighborEmbedding(hidden_channels, cutoff_lower, cutoff_upper, self.x_dimension).jittable() if neighbor_embedding else None

        self.edge_embedding = EdgeEmbedding().jittable()

        self.attention_layers = nn.ModuleList()
        block_params = dict(hidden_channels=hidden_channels, distance_influence=distance_influence,
                            num_heads=num_heads, activation=act_class, attn_activation=attn_activation,
                            cutoff_lower=cutoff_lower, cutoff_upper=cutoff_upper,
                            vecnorm_trainable=vecnorm_trainable, vecnorm_type=vecnorm_type,
                            dropout=dropout)
        
        for _ in range(num_layers - 1):
            layer = EquivariantMultiHeadAttention(**block_params,last_layer=False).jittable()
            self.attention_layers.append(layer)
        self.attention_layers.append(EquivariantMultiHeadAttention(**block_params,last_layer=True).jittable())

        self.x_out_norm = nn.LayerNorm(hidden_channels)
        self.v_out_norm = VecLayerNorm(hidden_channels, vecnorm_trainable, vecnorm_type)

        self.reset_parameters()

    def reset_parameters(self):
        self.atom_embedding.reset_parameters()
        if self.bond_embedding is not None:
            self.bond_embedding.reset_parameters()
        self.distance_expansion.reset_parameters()
        nn.init.xavier_uniform_(self.rbf_proj.weight)
        self.rbf_proj.bias.data.fill_(0)
        if self.neighbor_embedding is not None:
            self.neighbor_embedding.reset_parameters()
        for attn in self.attention_layers:
            attn.reset_parameters()
        self.x_out_norm.reset_parameters()
        self.v_out_norm.reset_parameters()

    def forward(self,
                data: Data,
                **kwargs
                ) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]:
        """Implements the forward pass of the Vector-Scalar Interactive Graph Neural Network (ViSNet) block."""
        raise NotImplementedError("Function has been masked for testing purposes")

    def __repr__(self):
        return (
            f"{self.__class__.__name__}("
            f"hidden_channels={self.hidden_channels}, "
            f"num_layers={self.num_layers}, "
            f"num_rbf={self.num_rbf}, "
            f"rbf_type={self.rbf_type}, "
            f"trainable_rbf={self.trainable_rbf}, "
            f"activation={self.activation}, "
            f"attn_activation={self.attn_activation}, "
            f"neighbor_embedding={self.neighbor_embedding}, "
            f"num_heads={self.num_heads}, "
            f"distance_influence={self.distance_influence}, "
            f"cutoff_lower={self.cutoff_lower}, "
            f"cutoff_upper={self.cutoff_upper})"
        )


class EquivariantMultiHeadAttention(MessagePassing):
    def __init__(
        self,
        hidden_channels,
        distance_influence,
        num_heads,
        activation,
        attn_activation,
        cutoff_lower,
        cutoff_upper,
        vecnorm_type,
        vecnorm_trainable,
        last_layer=False,
        dropout=0.0,
    ):
        super(EquivariantMultiHeadAttention, self).__init__(aggr="add", node_dim=0)
        assert hidden_channels % num_heads == 0, (
            f"The number of hidden channels ({hidden_channels}) "
            f"must be evenly divisible by the number of "
            f"attention heads ({num_heads})"
        )

        self.distance_influence = distance_influence
        self.num_heads = num_heads
        self.hidden_channels = hidden_channels
        self.head_dim = hidden_channels // num_heads
        self.last_layer = last_layer
        self.vecnorm_type = vecnorm_type
        self.vecnorm_trainable = vecnorm_trainable

        self.x_layernorm = nn.LayerNorm(hidden_channels)  # 归一化x
        self.f_layernorm = nn.LayerNorm(hidden_channels)
        self.v_layernorm = VecLayerNorm(hidden_channels, vecnorm_trainable, vecnorm_type)
        self.act = activation()
        self.attn_activation = act_class_mapping[attn_activation]()
        self.cutoff = CosineCutoff(cutoff_lower, cutoff_upper)

        self.q_proj = nn.Linear(hidden_channels, hidden_channels)
        self.k_proj = nn.Linear(hidden_channels, hidden_channels)
        self.v_proj = nn.Linear(hidden_channels, hidden_channels)
        self.s_proj = nn.Linear(hidden_channels, hidden_channels * 2)

        self.v_dot_proj = nn.Linear(hidden_channels, hidden_channels)
        
        if not self.last_layer:
            self.f_proj = nn.Linear(hidden_channels, hidden_channels * 2)
            self.src_proj = nn.Linear(hidden_channels, hidden_channels, bias=False)
            self.trg_proj = nn.Linear(hidden_channels, hidden_channels, bias=False)
            self.w_dot_proj = nn.Linear(hidden_channels, hidden_channels)

        self.o_proj = nn.Linear(hidden_channels, hidden_channels * 3)
        self.vec_proj = nn.Linear(hidden_channels, hidden_channels * 3, bias=False)

        self.dk_proj = None
        if distance_influence in ["keys", "both"]:
            self.dk_proj = nn.Linear(hidden_channels, hidden_channels)

        self.dv_proj = None
        if distance_influence in ["values", "both"]:
            self.dv_proj = nn.Linear(hidden_channels, hidden_channels)
            
        self.scalar_dropout = nn.Dropout(dropout)
        self.vector_dropout = nn.Dropout2d(dropout)

        self.reset_parameters()
        
    def vector_rejection(self, vec, d_ij):
        """Implements vector rejection operation for maintaining equivariance in geometric vector features."""
        raise NotImplementedError("Function has been masked for testing purposes")

    def reset_parameters(self):
        self.x_layernorm.reset_parameters()
        self.f_layernorm.reset_parameters()
        self.v_layernorm.reset_parameters()



Complete the function implementation replacing "raise NotImplementedError".