“_”在 lambda 函数中是什么意思，为什么要使用它？

我有一个以“_”为参数的匿名函数，我不知道它是什么意思以及为什么在这里使用它。

和功能是：

f = lambda _: model.loss(X, y)[0]

grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)

模型损失：

def loss(self, X, y=None):


    # Unpack variables from the params dictionary
    W1, b1 = self.params['W1'], self.params['b1']
    W2, b2 = self.params['W2'], self.params['b2']

    h1, h1_cache = affine_relu_forward(X, W1, b1)
    scores, h2_cache = affine_forward(h1, W2, b2)


    # If y is None then we are in test mode so just return scores
    if y is None:
        return scores

    loss, grads = 0, {}


    loss, dscores = softmax_loss(scores, y)
    loss = loss + 0.5*self.reg*(np.sum(W2**2) + np.sum(W1**2))
    dh1, grads['W2'], grads['b2'] = affine_backward(dscores,h2_cache)
    dX, grads['W1'], grads['b1'] = affine_relu_backward(dh1,h1_cache)
    grads['W1'] += self.reg*W1
    grads['W2'] += self.reg*W2

    return loss, grads

和函数eval_numerical_gradient：

def eval_numerical_gradient(f, x, verbose=True, h=0.00001):

    fx = f(x) # evaluate function value at original point
    grad = np.zeros_like(x)
    # iterate over all indexes in x
    it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])
    while not it.finished:

        # evaluate function at x+h
        ix = it.multi_index
        oldval = x[ix]
        x[ix] = oldval + h # increment by h
        fxph = f(x) # evalute f(x + h)
        x[ix] = oldval - h
        fxmh = f(x) # evaluate f(x - h)
        x[ix] = oldval # restore

        # compute the partial derivative with centered formula
        grad[ix] = (fxph - fxmh) / (2 * h) # the slope
        if verbose:
            print(ix, grad[ix])
        it.iternext() # step to next dimension

    return grad

损失函数并不复杂，我想知道其中的“_”代表什么和函数。