在    def forward_features(self, z, x, event_z, event_x,inference_params=None, if_random_cls_token_position=False, if_random_token_rank=False):
        x = self.hin_block(x)
        z = self.hin_block(z)

        x = self.patch_embed(x)                  #x.shape = torch.Size([B, 3, 256, 256])  -> torch.Size([2, 256, 384])
        z = self.patch_embed(z)                  #z.shape = torch.Size([B, 3, 128, 128])  -> torch.Size([2, 64, 384])
        B, M, _ = x.shape
       
        if self.if_cls_token:                 # False
            if self.use_double_cls_token:
                cls_token_head = self.cls_token_head.expand(B, -1, -1)
                cls_token_tail = self.cls_token_tail.expand(B, -1, -1)
                token_position = [0, M + 1]
                x = torch.cat((cls_token_head, x, cls_token_tail), dim=1)
                M = x.shape[1]
            else:
                if self.use_middle_cls_token:
                    cls_token = self.cls_token.expand(B, -1, -1)
                    token_position = M // 2
                    # add cls token in the middle
                    x = torch.cat((x[:, :token_position, :], cls_token, x[:, token_position:, :]), dim=1)       
                elif if_random_cls_token_position:
                    cls_token = self.cls_token.expand(B, -1, -1)
                    token_position = random.randint(0, M)
                    x = torch.cat((x[:, :token_position, :], cls_token, x[:, token_position:, :]), dim=1)
                    print("token_position: ", token_position)
                else:
                    cls_token = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
                    token_position = 0
                    x = torch.cat((cls_token, x), dim=1)
                M = x.shape[1]                 
       
        if self.if_abs_pos_embed:                  # True 
            x = x + self.pos_embed_x               # x = x + positon_embemding =torch.Size([B, 256, 384]) + torch.Size([1, 256, 384]) = torch.Size([B, 256, 384])
            z = z + self.pos_embed_z               # z = z + positon_embemding =torch.Size([B, 64, 384]) + torch.Size([1, 64, 384]) = torch.Size([B, 64, 384])
            x = torch.cat((z, x), dim=1)           # torch.Size([B, 320, 384])
            x = self.pos_drop(x)                   # x.shape = torch.Size([B, 320, 384])
            
        if if_random_token_rank:                   #False
            # 生成随机 shuffle 索引
            shuffle_indices = torch.randperm(M)

            if isinstance(token_position, list):
                print("original value: ", x[0, token_position[0], 0], x[0, token_position[1], 0])
            else:
                print("original value: ", x[0, token_position, 0])
            print("original token_position: ", token_position)

            # 执行 shuffle
            x = x[:, shuffle_indices, :]

            if isinstance(token_position, list):
                # 找到 cls token 在 shuffle 之后的新位置
                new_token_position = [torch.where(shuffle_indices == token_position[i])[0].item() for i in range(len(token_position))]
                token_position = new_token_position
            else:
                # 找到 cls token 在 shuffle 之后的新位置
                token_position = torch.where(shuffle_indices == token_position)[0].item()

            if isinstance(token_position, list):
                print("new value: ", x[0, token_position[0], 0], x[0, token_position[1], 0])
            else:
                print("new value: ", x[0, token_position, 0])
            print("new token_position: ", token_position)


        if_flip_img_sequences = False
        if self.flip_img_sequences_ratio > 0 and (self.flip_img_sequences_ratio - random.random()) > 1e-5:        # False
            x = x.flip([1])
            if_flip_img_sequences = True

        # mamba impl
        residual = None
        hidden_states = x
        if not self.if_bidirectional:                                 # True
            for layer_idx, layer in self.layers:
                if if_flip_img_sequences and self.if_rope:            # False
                    hidden_states = hidden_states.flip([1])
                    if residual is not None:
                        residual = residual.flip([1])

                # rope about
                if self.if_rope:                                       # False
                    hidden_states = self.rope(hidden_states)
                    if residual is not None and self.if_rope_residual:
                        residual = self.rope(residual)

                if if_flip_img_sequences and self.if_rope:             # False
                    hidden_states = hidden_states.flip([1])
                    if residual is not None:
                        residual = residual.flip([1])
                hidden_states, residual = layer(
                    hidden_states, residual, inference_params=inference_params
                )
        
        else:             # False
            # get two layers in a single for-loop
            for i in range(len(self.layers) // 2):
                if self.if_rope:
                    hidden_states = self.rope(hidden_states)
                    if residual is not None and self.if_rope_residual:
                        residual = self.rope(residual)

                hidden_states_f, residual_f = self.layers[i * 2](
                    hidden_states, residual, inference_params=inference_params
                )
                hidden_states_b, residual_b = self.layers[i * 2 + 1](
                    hidden_states.flip([1]), None if residual == None else residual.flip([1]), inference_params=inference_params
                )
                hidden_states = hidden_states_f + hidden_states_b.flip([1])
                residual = residual_f + residual_b.flip([1])
      
        if not self.fused_add_norm:         #False
            if residual is None:
                residual = hidden_states
            else:
                residual = residual + self.drop_path(hidden_states)
            hidden_states = self.norm_f(residual.to(dtype=self.norm_f.weight.dtype))
        else:       #True
            fused_add_norm_fn = rms_norm_fn if isinstance(self.norm_f, RMSNorm) else layer_norm_fn
            hidden_states = fused_add_norm_fn(                                         # hidden_states.shape = torch.Size([B, 320, 384])
                self.drop_path(hidden_states),
                self.norm_f.weight,
                self.norm_f.bias,
                eps=self.norm_f.eps,
                residual=residual,
                prenorm=False,
                residual_in_fp32=self.residual_in_fp32,
            )
        if self.if_cls_token:          #False
            if self.use_double_cls_token:
                return (hidden_states[:, token_position[0], :] + hidden_states[:, token_position[1], :]) / 2
            else:
                if self.use_middle_cls_token:
                    return hidden_states[:, token_position, :]
                elif if_random_cls_token_position:
                    return hidden_states[:, token_position, :]
                else:
                    return hidden_states[:, token_position, :]

        if self.final_pool_type == 'none':
            return hidden_states[:, -1, :]
        elif self.final_pool_type == 'mean':         #True
            return hidden_states.mean(dim=1)         #hidden_states.shape = torch.Size([2, 384])
        elif self.final_pool_type == 'max':
            return hidden_states
        elif self.final_pool_type == 'all':
            return hidden_states
        else:
            raise NotImplementedError中仅仅对x和z做了处理，那么现在将所有的操作复制一下，即同样的处理event_z和event_x