MXNet sym.Convolution can run even when the declared shape of variable is incorrect

Description

mx.sym.Convolution can still work even when the shape of mx.sym.Variable is incorrect.

To Reproduce

Steps to reproduce

import mxnet as mx
net = mx.symbol.Variable('data')
net = mx.sym.reshape(data=net, shape=(0, 10, 10, 3))
net = mx.sym.transpose(data=net, axes=[0,3,1,2])

weight = mx.symbol.Variable("conv2d_1/kernel1", shape=(3, 1e+10, 3, 3), stype="default", dtype=np.float32)
weight = mx.symbol.transpose(data=weight, axes=[0,1,2,3])
bias = mx.symbol.Variable("conv2d_1/bias1", shape=(3,))
net = mx.symbol.Convolution(data=net, name="conv2d_1/conv2d3",
                              kernel=(3, 3), stride=(1, 1), pad=(),
                              num_filter=3, weight=weight,
                              dilate=(1,1), bias=bias)
import numpy as np
input_shape = (10, 10, 10, 3)
x = np.random.rand(*input_shape)
e = net.bind(mx.cpu(), {'data': mx.nd.array(x), "conv2d_1/kernel1": mx.nd.array(np.random.rand(3,3,3,3)), "conv2d_1/bias1": mx.nd.array(np.random.rand(3,))})
e.forward()

When running the above code, the symbolic model e can still output a reasonable result even though I have declared the shape of the weight variable to be an unreasonable value: shape=(3, 1e+10, 3, 3). It seems that mxnet will not refer to the declared shape in mx.Variable, but intuitively speaking, setting the unreasonable variable shape while model can still run may be misleading.

I am wondering if you can add some shape checks between the declared variable shape and the actual bound data shape.

apache / mxnet