列表而不是元组作为返回：Numba jit-ted 函数中的失败类型推断

问：

我正在尝试使用 numba.jit 编译一个函数，该函数接受多个输入并返回一个元组。我在 jit 装饰中指定了输入和输出的类型，认为这会有所帮助。但是，我收到一条警告，说包含元组的列表无法转换为元组，并且 numba.jit 会回退到对象模式。

我正在构建一个脚本来模拟物理 2D 现象。下面是我尝试使用 numba.jit 编译的函数的代码（有点简化）。输入中未列出一些参数。这些是之前定义的常量参数，类型为 float，大小为 1。 这里的关键是我声明我将收到一个类型 Tuple（array（float64， 3d， C）， array（float64， 2d， A））。

@nb.jit(nb.types.containers.Tuple((nb.types.float64[:,:,::1],nb.types.float64[:,:]))(nb.types.float64,nb.types.containers.Tuple((nb.types.float64[:,:,:],nb.types.float64[:,:])),nb.types.float64[:,:,:],nb.types.float64[:,:,:],nb.types.float64[:,:],nb.types.float64[:,:,:],nb.types.float64[:,:]))
def model_f(t,dvar,dNdy,N_y,N_x,r,coeff2):
    
    X = dvar[0]
    X_ch = dvar[1]
    
    # trivial operations on matrix element-wise:
    i = 0
    for row in X[0,:,0]:
        j = 0
        for col in X[0,0,:]:
            N_y[:,i,j] = DGM_N(X,i,j)
            [r[:,i,j]] = RRates(X,T,i,j)
            j += 1
        i += 1
    
    # gradient of N_y:
    comp = 0
    for c_plane in X[:,0,0]:
        i = 0
        for row in X[0,:,0]:
            if i == 0:
                dNdy[comp,i,:] = (N_y[comp,i+1,:]-N_y[comp,i,:])/dy
            elif i == NY-1:
                dNdy[comp,i,:] = (N_y[comp,i,:]-N_y[comp,i-1,:])/dy
            else:
                dNdy[comp,i,:] = (N_y[comp,i+1,:]-N_y[comp,i-1,:])/(2*dy)
            i += 1
        comp += 1
    
    coeff1 = 1/(-dNdy+r)

    N_x = speed*X_ch

    i = 0
    for cell in x_axis:
        if i == 0:
            coeff2[:,i] = (-N_x[:,i]+No)/dx-N_y[:,0,i]/channel_height
        else:
            coeff2[:,i] = (-N_x[:,i]+N_x[:,i-1])/dx-N_y[:,0,i]/channel_height
        i += 1
    
    return [(coeff1,coeff2)]

但是，当我尝试编译函数时，出现以下错误：

NumbaWarning: 
Compilation is falling back to object mode WITH looplifting enabled because Function "model_f" failed type inference due to: No conversion from list(Tuple(array(float64, 3d, C), array(float64, 2d, A)))<iv=None> to Tuple(array(float64, 3d, C), array(float64, 2d, A)) for '$1656return_value.4', defined at None

File "soe_model_v2.1.py", line 338:
def model_f(t,dvar,T,mu_fuel,mu_air,dXdy,dNdy,N_y,N_x,r,Dkn,Deffbin,coeff2,result):
    <source elided>
    
    return [(coeff1,coeff2)]
    ^

During: typing of assignment at c:\users\matfe\...\soe_model_v2.1.py (338)

File "soe_model_v2.1.py", line 338:
def model_f(t,dvar,T,mu_fuel,mu_air,dXdy,dNdy,N_y,N_x,r,Dkn,Deffbin,coeff2,result):
    <source elided>
    
    return [(coeff1,coeff2)]
    ^

  @nb.jit(nb.types.containers.Tuple((nb.types.float64[:,:,::1],nb.types.float64[:,:]))(nb.types.float64,nb.types.containers.Tuple((nb.types.float64[:,:,:],nb.types.float64[:,:])),nb.types.float64,nb.types.float64,nb.types.float64,nb.types.float64[:,:,:],nb.types.float64[:,:,:],nb.types.float64[:,:,:],nb.types.float64[:,:],nb.types.float64[:,:,:],nb.types.float64[:],nb.types.float64[:,:],nb.types.float64[:,:],nb.types.containers.Tuple((nb.types.float64[:,:,:],nb.types.float64[:,:]))))

您可以看到，在原始函数代码中，我的函数有更多的输入，这些输入也是在 jit 修饰期间推断出来的（这使得错误消息很长），但这在这里应该无关紧要。

因此，我的“return”对象似乎被视为包含元组的列表，而不仅仅是元组。这是它不编译的原因吗？有人有什么建议吗？