OrderedDict type inference slow in Pytorch

Nobody has claimed this yet.

**Describe the bug**
Typechecking a file with with large OrderedDict variables is taking 14 seconds to analyze.

Steps:

clone https://github.com/pytorch/pytorch

run `pyright torch/ --verbose`

notice these two files are slow
```
[FG] Long operation: checking: file:///c%3A/dev/pytorch/torch/testing/_internal/common_methods_invocations.py (13595ms)
[FG] Long operation: checking: file:///c%3A/dev/pytorch/torch/utils/hipify/cuda_to_hip_mappings.py (14774ms)
```

the second file (https://github.com/pytorch/pytorch/blob/main/torch/utils/hipify/cuda_to_hip_mappings.py) contains large OrderedDict with no type annotations

```python
CUDA_TYPE_NAME_MAP = collections.OrderedDict(
[
("CUresult", ("hipError_t", CONV_TYPE, API_DRIVER)),
("cudaError_t", ("hipError_t", CONV_TYPE, API_RUNTIME)),
("cudaError", ("hipError_t", CONV_TYPE, API_RUNTIME)),
...
```

adding generic info cuts the time in half to 7 seconds
```python
CUDA_TYPE_NAME_MAP = collections.OrderedDict[str, tuple[str, int, int, int]](
```

adding annotations cuts the time in half, but 7 seconds still seems slow.
```
CUDA_TYPE_NAME_MAP: collections.OrderedDict[str, tuple[str, int, int, int]] = collections.OrderedDict(
```

I'm working on PR to update these in pytorch but thought maybe there is something pyright can improve on too.

should we warn when generic parameters are missing on collections.OrderedDict?

Reproduce the slowdown with `pyright torch/ --verbose` using `torch/testing/_internal/common_methods_invocations.py` and `torch/utils/hipify/cuda_to_hip_mappings.py`. Compare the reported times for unannotated, generic, and explicitly annotated OrderedDict declarations. Done means identifying a type-inference improvement or determining whether a missing-generic-parameter warning is appropriate.

Written by the indexing model from the issue text.