src/transformers/models/conditional_detr/modeling_conditional_detr.py/ConditionalDetrPreTrainedModel/_init_weights
class ConditionalDetrPreTrainedModel:    def _init_weights(self, module):
        std = self.config.init_std
        xavier_std = self.config.init_xavier_std

        if isinstance(module, ConditionalDetrMHAttentionMap):
            nn.init.zeros_(module.k_linear.bias)
            nn.init.zeros_(module.q_linear.bias)
            nn.init.xavier_uniform_(module.k_linear.weight, gain=xavier_std)
            nn.init.xavier_uniform_(module.q_linear.weight, gain=xavier_std)
        elif isinstance(module, ConditionalDetrLearnedPositionEmbedding):
            nn.init.uniform_(module.row_embeddings.weight)
            nn.init.uniform_(module.column_embeddings.weight)
        if isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()