[Feature Request] Float64 in onnx-runtime?

Nobody has claimed this yet.

### Describe the feature request

I am trying to use ONNX Runtime in inferencing where preision is very important.
We are training the model in pytorch and saving the weights in double. And exporting this model to onnx.
However looks like onnx runtime does not support double weights since it throws : [Unexpected input data type. Actual: (tensor(double)) , expected: (tensor(float))](https://stackoverflow.com/questions/68152634/unexpected-input-data-type-actual-tensordouble-expected-tensorfloat)
Note that: Our pytorch model is sequential, 6 layers deep, with SiLu activation.

### Describe scenario use case

We are using ONNX Runtime to run inferencing in financial data where extreme precision is needed to get the derivatives of smooth function by numerical methods.
Float64 will be ideal, not float32.

No source file, test, or entry point is named. Start by locating the inference path that rejects tensor(double) and compare the requested PyTorch-exported weights with the supported ONNX Runtime types; done means a float64 model can run without that type error, with coverage for the reported case.

Written by the indexing model from the issue text.