The "backward()" function is a method used in neural networks to compute the gradients of the loss function with respect to the weights and biases of the network. It is a crucial step in the process of training a neural network through backpropagation.

During the forward pass, the input data is passed through the network, and the "forward()" function calculates the output of the network. In the backward pass, the "backward()" function calculates the gradients of the loss function with respect to the output of each layer. These gradients are then used to adjust the weights and biases of the network in the direction that minimizes the loss function.

The "backward()" function typically takes in the output of the network and the target values, and computes the gradients of the loss function using techniques such as backpropagation or automatic differentiation.
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