onnxruntime

rope support 4D input tensor (#18454)

### Description
<!-- Describe your changes. -->

change RotaryEmbeddings op implementation, add support for 4D input
tensor that is with shape of [batch, num_heads, seq_len, head_size].

### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
Current RotaryEmbedding op only support 3d input tensor with shape
[batch, seq_len, hidden_size]

For llamav2 model, when using FusionRotaryEmbeddings to only fuse
RotaryEmbeddings op, there will be a transpose operation for query and
key, and then the input tensor of RotaryEmbeddings becomes 4D [batch,
num_heads, seq_len, head_size].

This scenario can't be supported by current RotaryEmbeddings
implementation. So it needs to support 4D input tensor.