校正以下代码的语法错误 def encode_edge(self, mode, node_history, node_history_st, edge_type, neighbors, neighbors_edge_value, first_history_indices, batch_size): max_hl = self.hyperparams['maximum_history_length'] max_neighbors = 0 for neighbor_states in neighbors: max_neighbors = max(max_neighbors, len(neighbor_states)) edge_states_list = list() # list of [#of neighbors, max_ht, state_dim] for i, neighbor_states in enumerate(neighbors): # Get neighbors for timestep in batch if len(neighbor_states) == 0: # There are no neighbors for edge type # TODO necessary? neighbor_state_length = int( np.sum([len(entity_dims) for entity_dims in self.state[edge_type[1]].values()]) ) edge_states_list.append(torch.zeros((1, max_hl + 1, neighbor_state_length), device=self.device)) else: edge_states_list.append(torch.stack(neighbor_states, dim=0).to(self.device)) # if self.hyperparams['edge_state_combine_method'] == 'sum': # Used in Structural-RNN to combine edges as well. op_applied_edge_states_list = list() for neighbors_state in edge_states_list: op_applied_edge_states_list.append(torch.sum(neighbors_state, dim=0))#torch.sum combined_neighbors = torch.stack(op_applied_edge_states_list, dim=0) # 获取combined_neighbors的第一个维度,代表邻接边的总数 combined_neighbors_0 = combined_neighbors.shape[0] # 创建全零矩阵s_next,形状为[batch_size, max_neighbors, combined_neighbors_0] s_next = torch.zeros((batch_size, max_neighbors, combined_neighbors_0), device=self.device) # 为s_next矩阵中每一行赋值为对应的combined_neighbors # for b in range(batch_size): # s_next[b, :len(neighbors[b]), :] = combined_neighbors[first_history_indices[b]] for i in range(batch_size): s_next[0, i, :] = batch_size[:] for i in range(max_neighbors): s_next[1, i, :] = max_neighbors[i, :] for i in range(combined_neighbors.shape[0]): s_next[2, i, :] = combined_neighbors
时间: 2023-03-01 15:35:50 浏览: 60
代码中缺少冒号,不是合法的 Python 代码。正确的代码应该是这样的:
```
def encode_edge(self, mode, node_history, node_history_st, edge_type, neighbors, neighbors_edge_value, first_history_indices, batch_size):
max_hl = self.hyperparams['maximum_history_length']
max_neighbors = 0
for neighbor_states in neighbors:
max_neighbors = max(max_neighbors, len(neighbor_states))
edge_states_list = list()
```
相关问题
async def encode_json(cls, content):
这段 Python 代码定义了一个异步函数 `encode_json`,它有两个参数:`cls` 和 `content`。其中,`cls` 是该函数所属的类,`content` 则是一个 Python 对象,用于表示要编码成 JSON 格式的数据。
该函数的作用是将 Python 对象 `content` 编码成 JSON 格式的字符串,并返回该字符串。由于该函数是异步函数,因此它可以在编码的过程中进行其他操作,例如等待网络请求的响应或执行其他异步任务。这在网络编程、异步 IO 等场景下非常常见。
需要注意的是,该函数中使用了 `async` 关键字来修饰函数定义,表明该函数是一个异步函数。在 Python 3.5 及以上的版本中,可以使用 `async/await` 关键字来实现异步编程,这使得编写异步程序变得更加简单和直观。同时,也需要在调用该函数的地方使用 `await` 关键字来等待函数执行完成。
转js代码 def __init__(self, key, iv): self.key = key.encode() self.iv = iv.encode() self.mode = AES.MODE_CBC
转换成JavaScript代码如下:
```javascript
class AESEnc {
constructor(key, iv) {
this.key = Buffer.from(key, 'utf8');
this.iv = Buffer.from(iv, 'utf8');
this.mode = 'aes-256-cbc';
}
}
```
在JavaScript中,没有直接等价于Python的`encode()`方法,所以我们使用`Buffer.from()`将字符串编码为字节。AES的CBC模式在JavaScript中使用`'aes-256-cbc'`表示。以上代码定义了一个名为`AESEncrypt`的类,具有与Python代码相同的构造函数逻辑。