Flop counter for matmul does not support matrix-vector product.

[`torch.matmul`](https://pytorch.org/docs/stable/generated/torch.matmul.html?highlight=matmul#torch.matmul) supports vector-vector and matrix-vector product. However, the second assertion in [`matmul_flop_jit()`](https://github.com/facebookresearch/fvcore/blob/main/fvcore/nn/jit_handles.py) seems to assume that the second input tensor is at least 2-dimensional.

```python
def matmul_flop_jit(inputs: List[Any], outputs: List[Any]) -> Number:
"""
Count flops for matmul.
"""
# Inputs should be a list of length 2.
# Inputs contains the shapes of two matrices.
input_shapes = [get_shape(v) for v in inputs]
assert len(input_shapes) == 2, input_shapes
assert input_shapes[0][-1] == input_shapes[1][-2], input_shapes
...
```

If the second input tensor is a 1-dimensional vector, an `IndexError: list index out of range` will be raised (due to `input_shapes[1][-2]`).

Is it possible to treat "the 2nd input is a vector" as a special case and compute FLOPs respectively?