requires_grad=true

这是PyTorch中的一个参数，用于指定一个tensor是否需要计算梯度。如果requires_grad=True，那么在进行反向传播时，这个tensor会自动记录梯度信息，并且可以通过调用tensor.grad来获取梯度值。如果requires_grad=False，那么这个tensor不会记录梯度信息，也无法进行反向传播。通常来说，模型参数需要requires_grad=True，而输入数据和中间变量可以设置requires_grad=False来减少内存消耗和加速计算。

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requires_grad=True

In PyTorch, the attribute `requires_grad=True` is used to specify that a tensor requires gradients to be computed during backpropagation.

When a tensor is created with `requires_grad=True`, PyTorch automatically tracks all the operations that involve that tensor and builds a computational graph. This graph is then used during backpropagation to compute the gradients of the loss with respect to the tensor.

By default, the attribute `requires_grad` is set to False when a tensor is created. However, you can set it to True using the following syntax:

import torch

x = torch.ones(3, 3, requires_grad=True)

In this example, the tensor `x` is created with a shape of (3, 3) and all its values are set to 1. The attribute `requires_grad` is set to True, which means that gradients will be computed during backpropagation.

requires_grad = true

'b'requires_grad = true'' 的中文翻译是 "requires_grad = True"。该函数通常用于设置一个变量是否需要计算梯度，该梯度将在反向传播过程中使用。当 requires_grad 设置为 True 时，PyTorch 将跟踪所有对变量的操作，并自动计算梯度。