def color_state(self): if self.assign_notice_state == '1': assign_state_name = '待签收' color_code = 'red' else: color_code = 'green' assign_state_name = '已签收' return format_html( '<span style="color:{};">{}</span>', color_code, assign_state_name, ) color_state.short_description = '签收状态'

这是一个 Python 类中的函数，它返回一个 HTML 标签，用于显示一个“签收状态”的文本，文本的颜色根据状态的不同而变化。如果状态为 1，则文本颜色为红色，状态为其他值时颜色为绿色。这个函数使用了 Django 模板语言中的 format_html 函数，这个函数可以将一个字符串格式化为 HTML 代码，并且会对其中的特殊字符进行转义，以防止 XSS 攻击。这个函数的 short_description 属性是 Django 中一个特殊的属性，它用于给这个函数添加一个简短的描述，通常用于在 Django 的后台管理界面中显示这个函数的名称。

相关问题

def apply_gradients(self, actor_gradients): return self.sess.run(self.optimize, feed_dict={ i: d for i, d in zip(self.actor_gradients, actor_gradients) }) def get_network_params(self): return self.sess.run(self.network_params) def set_network_params(self, input_network_params): self.sess.run(self.set_network_params_op, feed_dict={ i: d for i, d in zip(self.input_network_params, input_network_params) })请对这段代码进行优化

这段代码可以通过以下方式进行优化：

1. 使用 `*args` 和 `**kwargs` 可以使代码更加简洁和通用性更强。

2. 使用 `tf.Variable.assign()` 方法来设置网络参数，可以替代 `set_network_params()` 方法。

下面是优化后的代码：

def apply_gradients(self, *args, **kwargs):
    return self.sess.run(self.optimize, feed_dict={i: d for i, d in zip(self.actor_gradients, args)})

def get_network_params(self):
    return self.network_params

def set_network_params(self, input_network_params):
    for var, val in zip(self.network_params, input_network_params):
        var.assign(val)

这样代码更加简洁易读，并且使用了 TensorFlow 内置的方法来设置网络参数。

def connect(self): s = self.get_slice() if self.connected: return # increment connect attempt self.stat_collector.incr_connect_attempt(self) if s.is_avaliable(): s.connected_users += 1 self.connected = True print(f'[{int(self.env.now)}] Client_{self.pk} [{self.x}, {self.y}] connected to slice={self.get_slice()} @ {self.base_station}') return True else: self.assign_closest_base_station(exclude=[self.base_station.pk]) if self.base_station is not None and self.get_slice().is_avaliable(): # handover self.stat_collector.incr_handover_count(self) elif self.base_station is not None: # block self.stat_collector.incr_block_count(self) else: pass # uncovered print(f'[{int(self.env.now)}] Client_{self.pk} [{self.x}, {self.y}] connection refused to slice={self.get_slice()} @ {self.base_station}') return False def disconnect(self): if self.connected == False: print(f'[{int(self.env.now)}] Client_{self.pk} [{self.x}, {self.y}] is already disconnected from slice={self.get_slice()} @ {self.base_station}') else: slice = self.get_slice() slice.connected_users -= 1 self.connected = False print(f'[{int(self.env.now)}] Client_{self.pk} [{self.x}, {self.y}] disconnected from slice={self.get_slice()} @ {self.base_station}') return not self.connected def start_consume(self): s = self.get_slice() amount = min(s.get_consumable_share(), self.usage_remaining) # Allocate resource and consume ongoing usage with given bandwidth s.capacity.get(amount) print(f'[{int(self.env.now)}] Client_{self.pk} [{self.x}, {self.y}] gets {amount} usage.') self.last_usage = amount def release_consume(self): s = self.get_slice() # Put the resource back if self.last_usage > 0: # note: s.capacity.put cannot take 0 s.capacity.put(self.last_usage) print(f'[{int(self.env.now)}] Client_{self.pk} [{self.x}, {self.y}] puts back {self.last_usage} usage.') self.total_consume_time += 1 self.total_usage += self.last_usage self.usage_remaining -= self.last_usage self.last_usage = 0中的资源分配

要将这些函数修改为最小容量的资源分配，需要进行以下更改：

1. 在 `is_avaliable` 函数中，将 `bandwidth_next` 的计算方式修改为 `real_cap / (self.connected_users + 1) >= self.bandwidth_min`，即下一个用户的带宽大于等于最小容量。

2. 在 `start_consume` 函数中，将 `amount` 的计算方式修改为 `min(s.get_consumable_share(), self.usage_remaining, self.bandwidth_min)`，即需要分配的资源量为可用资源、剩余需求和最小容量的最小值。

修改后的代码如下：

def connect(self):
    s = self.get_slice()
    if self.connected:
        return
    # increment connect attempt
    self.stat_collector.incr_connect_attempt(self)
    if s.is_avaliable():
        s.connected_users += 1
        self.connected = True
        print(f"[{int(self.env.now)}] Client_{self.pk} [{self.x}, {self.y}] connected to slice={self.get_slice()} @ {self.base_station}")
        return True
    else:
        self.assign_closest_base_station(exclude=[self.base_station.pk])
        if self.base_station is not None and self.get_slice().is_avaliable():
            # handover
            self.stat_collector.incr_handover_count(self)
        elif self.base_station is not None:
            # block
            self.stat_collector.incr_block_count(self)
        else:
            pass # uncovered
        print(f"[{int(self.env.now)}] Client_{self.pk} [{self.x}, {self.y}] connection refused to slice={self.get_slice()} @ {self.base_station}")
        return False

def disconnect(self):
    if not self.connected:
        print(f"[{int(self.env.now)}] Client_{self.pk} [{self.x}, {self.y}] is already disconnected from slice={self.get_slice()} @ {self.base_station}")
    else:
        slice = self.get_slice()
        slice.connected_users -= 1
        self.connected = False
        print(f"[{int(self.env.now)}] Client_{self.pk} [{self.x}, {self.y}] disconnected from slice={self.get_slice()} @ {self.base_station}")
    return not self.connected

def start_consume(self):
    s = self.get_slice()
    amount = min(s.get_consumable_share(), self.usage_remaining, self.bandwidth_min)
    s.capacity.get(amount)
    print(f"[{int(self.env.now)}] Client_{self.pk} [{self.x}, {self.y}] gets {amount} usage.")
    self.last_usage = amount

def release_consume(self):
    s = self.get_slice()
    if self.last_usage > 0:
        s.capacity.put(self.last_usage)
        print(f"[{int(self.env.now)}] Client_{self.pk} [{self.x}, {self.y}] puts back {self.last_usage} usage.")
        self.total_consume_time += 1
        self.total_usage += self.last_usage
        self.usage_remaining -= self.last_usage
        self.last_usage = 0

def is_avaliable(self):
    real_cap = min(self.init_capacity, self.bandwidth_max)
    return real_cap / (self.connected_users + 1) >= self.bandwidth_min