Basic torch.nn.LSTM can be converted from traced model but not scripted model

Trying to convert an unmodified `torch.nn.LSTM` model through tracing and scripting. Conversion of the traced model works, conversion of the scripted model fails. A self-contained script to reproduce the error is given bellow.

## Trace

```
test_conversion.py:38:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/home/vagrant/.local/lib/python3.6/site-packages/coremltools/converters/_converters_entry.py:182: in convert
**kwargs
/home/vagrant/.local/lib/python3.6/site-packages/coremltools/converters/mil/converter.py:129: in mil_convert
ConverterRegistry, **kwargs)
/home/vagrant/.local/lib/python3.6/site-packages/coremltools/converters/mil/converter.py:171: in mil_convert_to_proto
prog = frontend_converter(model, **kwargs)
/home/vagrant/.local/lib/python3.6/site-packages/coremltools/converters/mil/converter.py:85: in __call__
return load(*args, **kwargs)
/home/vagrant/.local/lib/python3.6/site-packages/coremltools/converters/mil/frontend/torch/load.py:70: in load
converter = TorchConverter(torchscript, inputs, outputs, cut_at_symbols)
/home/vagrant/.local/lib/python3.6/site-packages/coremltools/converters/mil/frontend/torch/converter.py:145: in __init__
raw_graph, params_dict = self._expand_and_optimize_ir(self.torchscript)
/home/vagrant/.local/lib/python3.6/site-packages/coremltools/converters/mil/frontend/torch/converter.py:263: in _expand_and_optimize_ir
torchscript.forward.graph, torchscript._c
/home/vagrant/.local/lib/python3.6/site-packages/torch/jit/_script.py:558: in __getattr__
return super(RecursiveScriptModule, self).__getattr__(attr)
/home/vagrant/.local/lib/python3.6/site-packages/torch/jit/_script.py:288: in __getattr__
return super(ScriptModule, self).__getattr__(attr)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

self = RecursiveScriptModule(original_name=LSTM), name = 'forward'

def __getattr__(self, name: str) -> Union[Tensor, 'Module']:
if '_parameters' in self.__dict__:
_parameters = self.__dict__['_parameters']
if name in _parameters:
return _parameters[name]
if '_buffers' in self.__dict__:
_buffers = self.__dict__['_buffers']
if name in _buffers:
return _buffers[name]
if '_modules' in self.__dict__:
modules = self.__dict__['_modules']
if name in modules:
return modules[name]
raise ModuleAttributeError("'{}' object has no attribute '{}'".format(
> type(self).__name__, name))
E torch.nn.modules.module.ModuleAttributeError: 'RecursiveScriptModule' object has no attribute 'forward'
```

## To Reproduce
```python
import pytest
import torch
from torch import nn
import coremltools as ct

@pytest.fixture
def lstm_model():
model = nn.LSTM(3, 3)
model.eval()
return model

def test_lstm_run(lstm_model):
seq_len = 12
inputs = torch.randn(seq_len, 1, 3)
hidden = (torch.randn(1, 1, 3), torch.randn(1, 1, 3))
output, hidden = lstm_model(inputs, hidden)
assert list(output.shape) == [seq_len, 1, 3]

def test_lstm_traced(lstm_model):
inputs = torch.randn(12, 1, 3)
traced_model = torch.jit.trace(lstm_model, inputs)

ct.convert(
model=traced_model,
inputs=[
ct.TensorType(name="sequence", shape=(ct.RangeDim(1, 50), 1, 3))
]
)

def test_lstm_scripted(lstm_model):
scripted_model = torch.jit.script(lstm_model)

ct.convert(
model=scripted_model,
inputs=[
ct.TensorType(name="sequence", shape=(ct.RangeDim(1, 50), 1, 3))
]
)
```

## System environment (please complete the following information):
- coremltools version: 4.1
- How you install python (anaconda, virtualenv, system): system
- python version (e.g. 3.7): python3.6
- any other relevant information:
- torch version: 1.7

Start by running the self-contained test_lstm_scripted reproduction with coremltools 4.1, Python 3.6, and PyTorch 1.7. Inspect the Torch frontend around _expand_and_optimize_ir and the scripted-model graph handling shown in the traceback; done means the scripted torch.nn.LSTM converts successfully and the reproduction provides regression coverage.

Written by the indexing model from the issue text.