def temporal_affine_backward(dout, cache):
    """
    Backward pass for temporal affine layer.

    Input:
    - dout: Upstream gradients of shape (N, T, M)
    - cache: Values from forward pass

    Returns a tuple of:
    - dx: Gradient of input, of shape (N, T, D)
    - dw: Gradient of weights, of shape (D, M)
    - db: Gradient of biases, of shape (M,)
    """

    x, w, b, out = cache
    N, T, D = x.shape
    M = b.shape[0]

    dx = dout.reshape(N * T, M).dot(w.T).reshape(N, T, D)
    dw = dout.reshape(N * T, M).T.dot(x.reshape(N * T, D)).T
    db = dout.sum(axis=(0, 1))

    return dx, dw, db