I have a tflite model with float32 feature map and int8 weights, when I look into convert_conv() in tflite frontend, I found that _qnn.op.conv2d is constructed only if feature map is int8(uint8) typed. So, I tried to modify code like:

```
if (input_tensor.qnn_params or weight_tensor.qnn_params):
qnn_conv2d_params = dict(params)
qnn_conv2d_params["input_zero_point"] = input_tensor.qnn_params["zero_point"] if input_tensor.qnn_params else relay.const(0, "int32")
qnn_conv2d_params["kernel_zero_point"] = weight_tensor.qnn_params["zero_point"] if weight_tensor.qnn_params else relay.const(0, "int32")
qnn_conv2d_params["out_dtype"] = "int32" if (input_tensor.qnn_params and weight_tensor.qnn_params) else "float32"
qnn_conv2d_params["input_scale"] = input_tensor.qnn_params["scale"] if input_tensor.qnn_params else relay.const(1, "float32")
qnn_conv2d_params["kernel_scale"] = weight_tensor.qnn_params["scale"] if weight_tensor.qnn_params else relay.const(1, "float32")
out = _qnn.op.conv2d(in_expr, weight_expr, **qnn_conv2d_params)
else:
out = _op.nn.conv2d(in_expr, weight_expr, **params)
```

And I found that another check in QnnConv2DRel() has failed: “Expected qnn conv2d type(int8, uint8) for input but was float32”. Is it ok to simply modify code like I mentioned above and is it ok to ignore the ICHECK()? I don’t know are there any other inner constraints for the mix-precision issue, and I hope somebody could support this feature officially.