Gradient computation

Question

I am beginner in data-science. I am trying to understand this PyTorch code for gradient computation using custom autograd function:

class MyReLU(torch.autograd.Function):

@staticmethod
def forward(ctx, x):

    ctx.save_for_backward(x)
    return x.clamp(min=0)

def backward(ctx, grad_output):

    x, = ctx.saved_tensors
    grad_x = grad_output.clone()
    grad_x[x < 0] = 0
    return grad_x

However, I don't understand this line : grad_x[x < 0] = 0. Can anyone explain this part?