NN.Runtime.PyTorch.Import

Reusable PyTorch weight-import surface.

The general import path is JSON-first:

1. PyTorch loads the original checkpoint / state_dict.
2. The adapter emitted by NN.Runtime.PyTorch.Export.StateDict writes nested-list JSON.
3. Import.Core parses that JSON into shape-checked TorchLean tensors.

For graphs, the matching path is:

1. PyTorch captures an nn.Module with the adapter emitted by NN.Runtime.PyTorch.Export.TorchExport.
2. Import.TorchExport parses the resulting torchlean.ir.v1 graph JSON into NN.IR.Graph.
3. The parser runs the shared IR well-formedness and shape checkers before accepting the graph.

Op kind strings in that artifact are resolved through NN.Runtime.PyTorch.Wire, the same table the exporters write with; parsing a serialized operator tag recovers its NN.IR.OpTag identity.

This is the supported graph-import path today. The ONNX adapter emitted by NN.Runtime.PyTorch.Export.ONNX follows the same rule: lower ONNX nodes into the torchlean.ir.v1 artifact, then reuse this parser and the shared IR validators. Keeping the artifact boundary explicit avoids a second, looser graph semantics.

Graph import and payload import remain separate phases. A Conv/Gemm/BatchNorm graph can be validated as TorchLean IR once the adapter expands it into supported nodes; executing the graph then requires the matching constants/linear/conv payload store.

Import.MLP, Import.CNN, and Import.Transformer provide model-family adapters. Their runnable examples and reference artifacts live under NN.Examples.Interop.PyTorch.