[Docs] Fix warnings in mkdocs build (continued) (#24791)

Signed-off-by: Zerohertz <ohg3417@gmail.com>

vllm/model_executor/models/moonvit.py

@@ -70,11 +70,15 @@ def multihead_attention(
     v: torch.Tensor,
     q_cu_seqlens: Optional[torch.Tensor] = None,
     k_cu_seqlens: Optional[torch.Tensor] = None,
-):
+) -> torch.Tensor:
     """Multi-head attention using flash attention 2.
 
     Args:
-        q, k, v: tensor of shape (batch_size, seqlen, num_heads, head_dim),
+        q: Query tensor of shape (batch_size, seqlen, num_heads, head_dim),
+            or (tot_seqlens, num_heads, head_dim) if packing.
+        k: Key tensor of shape (batch_size, seqlen, num_heads, head_dim),
+            or (tot_seqlens, num_heads, head_dim) if packing.
+        v: Value tensor of shape (batch_size, seqlen, num_heads, head_dim),
             or (tot_seqlens, num_heads, head_dim) if packing.
         q_cu_seqlens (torch.Tensor): cumulative sequence lengths of q.
             The first element should be 0 and the last element should be q.shape[0].
@@ -123,8 +127,14 @@ def sdpa_attention(
     """SDPA attention.
 
     Args:
-        q, k, v: tensor of shape (batch_size, seqlen, num_heads, head_dim),
+        q: Query tensor of shape (batch_size, seqlen, num_heads, head_dim),
             or (tot_seqlens, num_heads, head_dim) if packing.
+        k: Key tensor of shape (batch_size, seqlen, num_heads, head_dim),
+            or (tot_seqlens, num_heads, head_dim) if packing.
+        v: Value tensor of shape (batch_size, seqlen, num_heads, head_dim),
+            or (tot_seqlens, num_heads, head_dim) if packing.
+        q_cu_seqlens: Optional cumulative sequence lengths of q.
+        k_cu_seqlens: Optional cumulative sequence lengths of k.
     """
     seq_length = q.shape[0]
     attention_mask = torch.zeros([1, seq_length, seq_length],
@@ -387,7 +397,7 @@ class MLP2(nn.Module):
     def __init__(self,
                  dims: list[int],
                  activation,
-                 bias=True,
+                 bias: bool = True,
                  prefix: str = "",
                  use_data_parallel: bool = False):
         super().__init__()