Hi all, I’m new to MXNet and coming across some issues.

I am surprised to find no answers yet in the list of available questions. It seems like a pretty common problem and pretty easy to solve in Tensorflow.

Put it simple, I’m loading a network, then trying to run inference with varying batch_sizes, height and width of image.

```
import mxnet as mx
sym, arg, aux = mx.model.load_checkpoint("simple_pose_resnet50_v1d", 0)
mod = mx.mod.Module(symbol=sym, label_names=None)
mod.bind(for_training=False, data_shapes=[('data', (-1,3,-1,-1))])
mod.set_params(arg, aux)
from collections import namedtuple
Batch = namedtuple('Batch', ['data'])
mod.forward(Batch([pose_input]))
predicted_heatmap = mod.get_outputs()
```

I’ve highlighted -1 as a common notation in Tensorflow for unknown size and to be determined doing runtime. Is it possible to do this in MXNet? Please help