TrainingExtensions/torch/src/python/aimet_torch/meta/connectedgraph.py
def _parse_top_level_trace(self, trace: Union[torch.jit.TopLevelTracedModule, torch.jit.TracedModule],
                               model: torch.nn.Module) -> Tuple[List[IrNode], Dict[torch._C.TensorType,
                                                                                   torch._C.TensorType]]:
        """
        Parse the top level trace and return a list of IrNodes and a dictionary mapping equivalent output tensors found
        during trace parsing.
        :param trace: Pytorch JIT trace for model or a submodule
        :param model: Pytorch model to create connected graph from
        :return: Tuple containing a list of IrNodes and a dictionary mapping equivalent output tensors found during
        trace parsing.
        """
        ir_nodes_list = []
        output_map = {}
        _ = self._parse_trace_graph(trace, model, ir_nodes_list=ir_nodes_list, output_map=output_map)
        return ir_nodes_list, output_map

        Parse the top level trace and return a list of IrNodes and a dictionary mapping equivalent output tensors found
        during trace parsing.
        :param trace: Pytorch JIT trace for model or a submodule
        :param model: Pytorch model to create connected graph from
        :return: Tuple containing a list of IrNodes and a dictionary mapping equivalent output tensors found during
        trace parsing.
        
Parse the top level trace and return a list of IrNodes and a dictionary mapping equivalent output tensors found
during trace parsing.
Parse the top level trace and return a list of IrNodes and a dictionary mapping equivalent output tensors found during trace parsing .
def _parse_top_level_trace(self, trace: Union[torch.jit.TopLevelTracedModule, torch.jit.TracedModule],
                               model: torch.nn.Module) -> Tuple[List[IrNode], Dict[torch._C.TensorType,
                                                                                   torch._C.TensorType]]:
        ir_nodes_list = []
        output_map = {}
        _ = self._parse_trace_graph(trace, model, ir_nodes_list=ir_nodes_list, output_map=output_map)
        return ir_nodes_list, output_map
def _parse_top_level_trace ( self , trace : Union [ torch . jit . TopLevelTracedModule , torch . jit . TracedModule ] , model : torch . nn . Module ) -> Tuple [ List [ IrNode ] , Dict [ torch . _C . TensorType , torch . _C . TensorType ] ] : ir_nodes_list = [ ] output_map = { } _ = self . _parse_trace_graph ( trace , model , ir_nodes_list = ir_nodes_list , output_map = output_map ) return ir_nodes_list , output_map
Parse the top level trace and return a list of IrNodes and a dictionary mapping equivalent output tensors found
during trace parsing.
Parse the top level trace and return a list of IrNodes and a dictionary mapping equivalent output tensors found during trace parsing .
"""
        Parse the top level trace and return a list of IrNodes and a dictionary mapping equivalent output tensors found
        during trace parsing.
        :param trace: Pytorch JIT trace for model or a submodule
        :param model: Pytorch model to create connected graph from
        :return: Tuple containing a list of IrNodes and a dictionary mapping equivalent output tensors found during
        trace parsing.
        """
Tuple containing a list of IrNodes and a dictionary mapping equivalent output tensors found during
trace parsing.
Tuple containing a list of IrNodes and a dictionary mapping equivalent output tensors found during trace parsing .
self
trace
Union[torch.jit.TopLevelTracedModule, torch.jit.TracedModule]
Pytorch JIT trace for model or a submodule
Pytorch JIT trace for model or a submodule
model
torch.nn.Module
Pytorch model to create connected graph from
Pytorch model to create connected graph from