[Pytorch] frontend full_impl fix (#14122)

Minor fix in pytorch frontend to compile gpt2 model, reproduce script.
torch_version = 1.13.1
transformers_version = 4.26.1

```
from transformers import GPT2LMHeadModel
import torch
import tvm
from tvm import relay

inp = torch.ones((1, 128)).to(torch.int64)
input_shapes = [("input_ids", ((1, 128), "int64"))]

model = GPT2LMHeadModel.from_pretrained('gpt2', return_dict=False)
trace_model = torch.jit.trace(model, inp, strict=False)
outputs = trace_model(inp)

mod, params = relay.frontend.from_pytorch(trace_model, input_shapes)
with tvm.transform.PassContext(opt_level=3):
    lib = relay.build(mod, target='llvm', params=params)

runtime = tvm.contrib.graph_executor.GraphModule(lib["default"](tvm.device('cpu', 0)))
runtime.set_input("input_ids", inp.numpy())
runtime.run()
out = runtime.get_output(0).numpy()
print(out)
print('Done...')
```

Before the fix, the error message
```
Traceback (most recent call last):
  File "gpt2_compile.py", line 13, in <module>
    mod, params = relay.frontend.from_pytorch(trace_model, input_shapes)
  File "/home/ubuntu/apache_tvm/tvm/python/tvm/relay/frontend/pytorch.py", line 4791, in from_pytorch
    outputs = converter.convert_operators(_get_operator_nodes(graph.nodes()), outputs, ret_name)
  File "/home/ubuntu/apache_tvm/tvm/python/tvm/relay/frontend/pytorch.py", line 4164, in convert_operators
    relay_out = relay_op(
  File "/home/ubuntu/apache_tvm/tvm/python/tvm/relay/frontend/pytorch.py", line 841, in full
    return self.full_impl(data, fill_value, dtype)
  File "/home/ubuntu/apache_tvm/tvm/python/tvm/relay/frontend/pytorch.py", line 743, in full_impl
    fill_value = _expr.const(fill_value, dtype=dtype)
  File "/home/ubuntu/apache_tvm/tvm/python/tvm/relay/expr.py", line 707, in const
    raise ValueError("value has to be scalar or NDArray")
ValueError: value has to be scalar or NDArray
```

because `fill_value` is
```
%0 = cast(64, dtype="float32");
power(%0, 0.5f)
```

python/tvm/relay/frontend/pytorch.py

@@ -738,6 +738,8 @@ def full_impl(self, data, fill_value, dtype):
             size = _op.concatenate(tmp, axis=0)
 
         if not isinstance(fill_value, _expr.Constant):
+            if isinstance(fill_value, _expr.Expr):
+                fill_value = _infer_value(fill_value, {})
             fill_value = _expr.const(fill_value, dtype=dtype)
         out = _op.full(fill_value, size, dtype=dtype)
         if need_reshape: