/** * Perform the specified type of NV config reset. The radio will be taken offline * and the device must be rebooted after the operation. Used for device * configuration by some CDMA operators. * * <p>Requires Permission: * {@link android.Manifest.permission#MODIFY_PHONE_STATE MODIFY_PHONE_STATE} or that the calling * app has carrier privileges (see {@link #hasCarrierPrivileges}). * * TODO: remove this one. use {@link #rebootRadio()} for reset type 1 and * {@link #resetRadioConfig()} for reset type 3 * * @param resetType reset type: 1: reload NV reset, 2: erase NV reset, 3: factory NV reset * @return true on success; false on any failure. * * @hide */ @UnsupportedAppUsage public boolean nvResetConfig(int resetType) { try { ITelephony telephony = getITelephony(); if (telephony != null) { if (resetType == 1 /*1: reload NV reset */) { return telephony.rebootModem(getSlotIndex()); } else if (resetType == 3 /3: factory NV reset /) { return telephony.resetModemConfig(getSlotIndex()); } else { Rlog.e(TAG, "nvResetConfig unsupported reset type"); } } } catch (RemoteException ex) { Rlog.e(TAG, "nvResetConfig RemoteException", ex); } catch (NullPointerException ex) { Rlog.e(TAG, "nvResetConfig NPE", ex); } return false; } / * Rollback modem configurations to factory default except some config which are in whitelist. * Used for device configuration by some carriers. * * <p>Requires Permission: * {@link android.Manifest.permission#MODIFY_PHONE_STATE MODIFY_PHONE_STATE} or that the calling * app has carrier privileges (see {@link #hasCarrierPrivileges}). * * @return {@code true} on success; {@code false} on any failure. * * @hide */ @RequiresPermission(Manifest.permission.MODIFY_PHONE_STATE) @SystemApi public boolean resetRadioConfig() { try { ITelephony telephony = getITelephony(); if (telephony != null) { return telephony.resetModemConfig(getSlotIndex()); } } catch (RemoteException ex) { Rlog.e(TAG, "resetRadioConfig RemoteException", ex); } catch (NullPointerException ex) { Rlog.e(TAG, "resetRadioConfig NPE", ex); } return false; }这两个接口是否可以恢复相关的网络

这两个接口都是用于恢复设备的网络配置，但具体恢复哪些网络配置取决于运营商的要求和设备的支持情况。

`nvResetConfig` 方法根据提供的重置类型（1、2、3），执行指定类型的 NV 配置重置操作。这个方法会将设备的无线电离线，并在操作完成后要求设备重启。该方法主要用于某些 CDMA 运营商的设备配置。但是，根据代码中的注释，这个方法将被废弃，建议使用 `rebootRadio()`（重置类型1）和 `resetRadioConfig()`（重置类型3）方法来代替。

`resetRadioConfig` 方法用于将设备的调制解调器配置回到出厂默认值，除了一些白名单中的配置。该方法主要用于某些运营商的设备配置。调用该方法会重置调制解调器的配置，但具体重置哪些配置取决于设备和运营商的实现。

需要注意的是，这两个方法都需要调用者具有相应的权限，如 `MODIFY_PHONE_STATE` 权限或具有载波特权。不具备这些权限的应用程序将无法调用这些方法。

总之，这两个接口可以恢复相关的网络配置，但具体恢复哪些配置以及是否适用取决于设备和运营商的要求。

相关问题

Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same

这个错误通常是由于输入的张量和权重的类型不匹配导致的。你可以使用 `to()` 方法将输入张量和权重转换为相同的类型。如果你的计算设备是 GPU，则可以使用 `.cuda()` 方法将它们都转换为 CUDA 张量类型。

以下是一个示例代码，用于将输入张量和权重转换为相同的 CUDA 张量类型：

import torch

input_tensor = torch.randn(10, 10).cuda()
weight = torch.randn(10, 10)

# Convert weight to CUDA tensor
weight = weight.cuda()

# Perform operation using input_tensor and weight
output = torch.mm(input_tensor, weight)

在这个示例中，`input_tensor` 是一个随机生成的大小为 `(10, 10)` 的 CUDA 张量，`weight` 是一个随机生成的大小为 `(10, 10)` 的 CPU 张量。通过使用 `weight.cuda()` 将 `weight` 转换为 CUDA 张量类型，使得 `input_tensor` 和 `weight` 的类型相同。然后可以使用 `torch.mm()` 函数执行矩阵乘法操作，并将结果保存在 `output` 变量中。

RuntimeError: Input type (torch.cuda.LongTensor) and weight type (torch.cuda.FloatTensor) should be the same

This error occurs when you are trying to perform an operation on a tensor and its corresponding weight, but their data types do not match. In this specific case, you are trying to perform an operation on a tensor of type `torch.cuda.LongTensor` and its corresponding weight of type `torch.cuda.FloatTensor`.

To resolve this error, you need to ensure that the data types of the tensor and its weight are the same. You can do this by converting one of them to match the other.

For example, if you want to convert the weight to type `torch.cuda.LongTensor`, you can use the `.long()` method to convert it:

weight = weight.long()

Alternatively, if you want to convert the tensor to type `torch.cuda.FloatTensor`, you can use the `.float()` method to convert it:

tensor = tensor.float()

Once you have converted one of the variables to match the other, you should be able to perform the operation without any issues.