How to use the numpy.empty function in numpy

def test_grad_type_check_shape(self):
        a = chainer.Variable(np.empty((3,), dtype=np.float32))
        with self.assertRaises(ValueError):
            a.grad = np.empty((2,), dtype=np.float32)

def interpol(a, inds, order=3, mode="nearest", mask_nan=False, cval=0.0, prefilter=True):
	"""Given an array a[{x},{y}] and a list of float indices into a,
	inds[len(y),{z}], returns interpolated values at these positions as [{x},{z}]."""
	a    = np.asanyarray(a)
	inds = np.asanyarray(inds)
	inds_orig_nd = inds.ndim
	if inds.ndim == 1: inds = inds[:,None]

	npre = a.ndim - inds.shape[0]
	res = np.empty(a.shape[:npre]+inds.shape[1:],dtype=a.dtype)
	fa, fr = partial_flatten(a, range(npre,a.ndim)), partial_flatten(res, range(npre, res.ndim))
	if mask_nan:
		mask = ~np.isfinite(fa)
		fa[mask] = 0
	for i in range(fa.shape[0]):
		fr[i].real = scipy.ndimage.map_coordinates(fa[i].real, inds, order=order, mode=mode, cval=cval, prefilter=prefilter)
		if np.iscomplexobj(fa[i]):
			fr[i].imag = scipy.ndimage.map_coordinates(fa[i].imag, inds, order=order, mode=mode, cval=cval, prefilter=prefilter)
	if mask_nan and np.sum(mask) > 0:
		fmask = np.empty(fr.shape,dtype=bool)
		for i in range(mask.shape[0]):
			fmask[i] = scipy.ndimage.map_coordinates(mask[i], inds, order=0, mode=mode, cval=cval, prefilter=prefilter)
		fr[fmask] = np.nan
	if inds_orig_nd == 1: res = res[...,0]
	return res

def initCoefficients(nLayers, nFrequencies, nCoefficients, frequencies, omega, lamda, lamda2, par):

    nL1 = nLayers + 1

    un = empty((nL1, nFrequencies, nCoefficients), dtype=complex128)
    Y = empty((nL1, nFrequencies, nCoefficients), dtype=complex128)
    Yn = empty((nL1, nFrequencies, nCoefficients), dtype=complex128)

    # Compute the Admitivity, yn=j*omega*eps+sigma
    for i in range(nFrequencies):
        oTmp = 1.0 / omega[i]
        for jc in range(nCoefficients):
            Y[0, i, jc] = oTmp

    p = par[0]
    for i in range(nFrequencies):
        tTmp = (omega[i] * eps0 + p) * omega[i]
        for jc in range(nCoefficients):
            unTmp = sqrt(tTmp + lamda2[i, jc])
            un[0, i, jc] = unTmp
            Yn[0, i, jc] = unTmp * Y[0, i, jc]

    for k in range(1, nL1):

qf1_target.load_state_dict(qf1.state_dict())
qf2_target.load_state_dict(qf2.state_dict())
q_optimizer = optim.Adam(list(qf1.parameters()) + list(qf2.parameters()), lr=args.learning_rate)
actor_optimizer = optim.Adam(list(actor.parameters()), lr=args.learning_rate)
loss_fn = nn.MSELoss()
exploration_noise = NormalActionNoise(np.zeros(output_shape)) if args.action_noise == "normal" else OrnsteinUhlenbeckActionNoise(np.zeros(output_shape))
policy_noise = NormalActionNoise(np.zeros(output_shape)) if args.action_noise == "normal" else OrnsteinUhlenbeckActionNoise(np.zeros(output_shape))

# TRY NOT TO MODIFY: start the game
global_step = 0
exploration_noise.sigma = 0.1
policy_noise.sigma = 0.2

while global_step < args.total_timesteps:
    next_obs = np.array(env.reset())
    actions = np.empty((args.episode_length,), dtype=object)
    rewards, dones = np.zeros((2, args.episode_length))
    td_losses = np.zeros(args.episode_length)
    obs = np.empty((args.episode_length,) + env.observation_space.shape)
    
    # TRY NOT TO MODIFY: prepare the execution of the game.
    for step in range(args.episode_length):
        global_step += 1
        obs[step] = next_obs.copy()

        # ALGO LOGIC: put action logic here
        if global_step < args.learning_starts:
            actions[step] = env.action_space.sample()
        else:
            action = actor.forward(obs[step:step+1])
            actions[step] = (action.tolist()[0] + exploration_noise()).clip(env.action_space.low, env.action_space.high)

log.debug2('memory = %s', mem_now)
        max_memory = max(2000, mydf.max_memory-mem_now)
        # nkptj for 3c-coulomb arrays plus 1 Lpq array
        buflen = min(max(int(max_memory*.6*1e6/16/naux/(nkptj+1)), 1), nao_pair)
        shranges = _guess_shell_ranges(cell, buflen, aosym)
        buflen = max([x[2] for x in shranges])
        # +1 for a pqkbuf
        if aosym == 's2':
            Gblksize = max(16, int(max_memory*.2*1e6/16/buflen/(nkptj+1)))
        else:
            Gblksize = max(16, int(max_memory*.4*1e6/16/buflen/(nkptj+1)))
        Gblksize = min(Gblksize, ngs, 16384)
        pqkRbuf = numpy.empty(buflen*Gblksize)
        pqkIbuf = numpy.empty(buflen*Gblksize)
        # buf for ft_aopair
        buf = numpy.empty((nkptj,buflen*Gblksize), dtype=numpy.complex128)

        col1 = 0
        for istep, sh_range in enumerate(shranges):
            log.debug1('int3c2e [%d/%d], AO [%d:%d], ncol = %d', \
                       istep+1, len(shranges), *sh_range)
            bstart, bend, ncol = sh_range
            col0, col1 = col1, col1+ncol
            j3cR = []
            j3cI = []
            for k, idx in enumerate(adapted_ji_idx):
                v = fuse(numpy.asarray(feri['j3c/%d'%idx][:,col0:col1]))
                if is_zero(kpt):
                    for i, c in enumerate(vbar):
                        if c != 0:
                            v[i] -= c * ovlp[k][col0:col1]
                j3cR.append(numpy.asarray(v.real, order='C'))

def _robust_regression(time, flux, flux_err, periods, loss, scale):
    """
    Periodogram peaks for a given loss function and scale.
    """
    beta0 = np.array([0, 1, 0])
    mu = np.median(flux)
    x = np.ones([len(time), len(beta0)])
    chi_model = np.empty([len(periods)])
    chi_noise = np.empty([len(periods)])

    for i in range(len(periods)):
        chi_model[i] = least_squares(
            _model,
            beta0,
            loss=loss,
            f_scale=scale,
            args=(x, periods[i], time, flux, flux_err),
        ).cost
        chi_noise[i] = least_squares(
            _noise, mu, loss=loss, f_scale=scale, args=(time, flux, flux_err)
        ).cost
    power = 1 - chi_model / chi_noise

    return power

self.pnet, self.rnet, self.onet,
            threshold_list, factor)
        # 模型得出的边界框
        box_2d_array_1 = box_2d_array.reshape(-1, 5)
        # 模型预测出box的4个值、box的置信度，共5个值
        box_2d_array_2 = box_2d_array_1[:, 0:4]
        box_list = []
        image_height, image_width, _ = image_3d_array.shape
        image_size = image_width, image_height
        for box in box_2d_array_2:
            new_box = get_new_box(box, margin, image_size)
            box_list.append(new_box)
        box_2d_array_3 = np.array(box_list).astype('int')
        # 模型得出的人脸5个关键点，即10个值
        if len(point_2d_array) == 0:
            point_2d_array_1 = np.empty((0, 10))
        else:
            point_2d_array_1 = np.transpose(point_2d_array, [1, 0])    
        return box_2d_array_3, point_2d_array_1

def f(N, D, M):
    X = np.random.randint(0, 5, size=(N, D)).astype(np.int32)
    y = np.empty(N, dtype=np.int32)
    for i in prange(N):
        y[i] = i % 4
    p = np.random.randint(0, 5, size=(M, D)).astype(np.int32)
    clf = hpat.ml.MultinomialNB(n_classes=4)
    t1 = time.time()
    clf.train(X, y)
    res = clf.predict(p)
    print("Exec time:", time.time() - t1)
    return res.sum()

def _grab_real(self, index=None):
        array = np.empty(self._record_shape, dtype=self._record_dtype)
        data = array.__array_interface__['data'][0]

        if index is not None:
            if self.uca.readout(data, index):
                return array
            else:
                raise base.CameraError('No frame available')

        if self.uca.grab(data):
            return array
        else:
            raise base.CameraError('No frame available')

bucket_sizes = [len(data[b]) for b in range(len(_buckets))]

  data_variable = []

  ss = [0] * len(_buckets)
  ss1 = [0] * len(_buckets)
  for bucket_id in range(len(_buckets)):
    bucket_size = bucket_sizes[bucket_id]
    if bucket_size == 0:
      data_variable.append((1, 1))
      continue

    bucket_length = _buckets[bucket_id]
    char_length = min(MAX_CHAR_LENGTH, max_char_length[bucket_id] + NUM_CHAR_PAD)
    wid_inputs = np.empty([bucket_size, bucket_length], dtype=np.int64)
    cid_inputs = np.empty([bucket_size, bucket_length, char_length], dtype=np.int64)
    pid_inputs = np.empty([bucket_size, bucket_length], dtype=np.int64)
    xpid_inputs = np.empty([bucket_size, bucket_length], dtype=np.int64)
    hid_inputs = np.empty([bucket_size, bucket_length], dtype=np.int64)
    tid_inputs = np.empty([bucket_size, bucket_length], dtype=np.int64)

    masks_inputs = np.zeros([bucket_size, bucket_length], dtype=np.float32)
    single_inputs = np.zeros([bucket_size, bucket_length], dtype=np.int64)

    lengths_inputs = np.empty(bucket_size, dtype=np.int64)
    
    order_inputs = np.empty(bucket_size, dtype=np.int64)
    raw_word_inputs, raw_lines = [], []

    for i, inst in enumerate(data[bucket_id]):
      ss[bucket_id]+=1
      ss1[bucket_id]=bucket_length