如何建立自己的南宁百度seo排名公司
AlphaFold3 msa_pairing 模块的 _correct_post_merged_feats 函数用于对合并后的特征进行修正，确保它们符合预期的格式和要求。这包括可能的对特征值进行调整或进一步的格式化，确保合并后的 FeatureDict 适合于后续模型的输入。
主要作用是：
- 在多链蛋白质 MSA（多序列比对）合并后，重新计算/调整某些特征：
seq_length（序列长度）num_alignments（MSA 比对的序列数）
- 为 MSA 生成合适的掩码（mask），用于模型训练：
cluster_bias_mask：控制 MSA 的 query 序列位置。bert_mask：用于 BERT-style MSA 预训练掩码。
源代码：
def _correct_post_merged_feats(np_example: Mapping[str, np.ndarray],np_chains_list: Sequence[Mapping[str, np.ndarray]],pair_msa_sequences: bool
) -> Mapping[str, np.ndarray]:"""Adds features that need to be computed/recomputed post merging."""np_example['seq_length'] = np.asarray(np_example['aatype'].shape[0],dtype=np.int32)np_example['num_alignments'] = np.asarray(np_example['msa'].shape[0],dtype=np.int32)if not pair_msa_sequences:# Generate a bias that is 1 for the first row of every block in the# block diagonal MSA - i.e. make sure the cluster stack always includes# the query sequences for each chain (since the first row is the query# sequence).cluster_bias_masks = []for chain in np_chains_list:mask = np.zeros(chain['msa'].shape[0])mask[0] = 1cluster_bias_masks.append(mask)np_example['cluster_bias_mask'] = np.concatenate(cluster_bias_masks)# Initialize Bert mask with masked out off diagonals.msa_masks = [np.ones(x['msa'].shape, dtype=np.float32)for x in np_chains_list]np_example['bert_mask'] = block_diag(*msa_masks, pad_value=0)else:np_example['cluster_bias_mask'] = np.zeros(np_example['msa'].shape[0])np_example['cluster_bias_mask'][0] = 1# Initialize Bert mask with masked out off diagonals.msa_masks = [np.ones(x['msa'].shape, dtype=np.float32) forx in np_chains_list]msa_masks_all_seq = [np.ones(x['msa_all_seq'].shape, dtype=np.float32) forx in np_chains_list]