Project: 016_RMP-SAM_Towards_Real-Time_Multi-Purpose_Segment_An | Function: mask_pool (iclr)

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:** Core mask pooling operation implementing pooling-based dynamic convolution that enables unified processing of object and prompt queries with efficient masking

**KEY TERMS:** {
  "mask_pooled_x": "pooled feature representation",
  "torch.einsum": "efficient tensor contraction for pooling",
  "denorm": "normalization term for stable pooling",
  "mask": "predicted mask for pooling guidance"
}

**CONTEXT CODE:** import torch
import torch.nn.functional as F


# https://github.com/NVlabs/ODISE/blob/e97b06c424c575fec9fc5368dd4b3e050d91abc4/odise/modeling/meta_arch/odise.py#L923

def mask_pool(x, mask):
    """
    Args:
        x: [B, C, H, W]
        mask: [B, Q, H, W]
    """
    raise NotImplementedError("Function implementation has been masked for testing")

Complete the function implementation replacing "raise NotImplementedError".