mirror of
https://github.com/NicolasBohn/NexQuant.git
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Xu Yang
1d9b4cd2ec
* use CoSTEER as component name * rename factorimplementation to avoid confusion * rename modelimplementation * align benchmark and evolving evaluators * add scenario to evaluator init function * rename all factorimplementationknowledge in CoSTEER * remove all scenario related information in component * remove useless code --------- Co-authored-by: xuyang1 <xuyang1@microsoft.com>
19 lines
1.1 KiB
YAML
19 lines
1.1 KiB
YAML
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code_implement_sys: -|
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You are an assistant whose job is to answer user's question."
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code_implement_user: -|
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With the following given information, write a python code using pytorch and torch_geometric to implement the model.
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This model is in the graph learning field, only have one layer.
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The input will be node_feature [num_nodes, dim_feature] and edge_index [2, num_edges] (It would be the input of the forward model)
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There is not edge attribute or edge weight as input. The model should detect the node_feature and edge_index shape, if there is Linear transformation layer in the model, the input and output shape should be consistent. The in_channels is the dimension of the node features.
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Implement the model forward function based on the following information:model formula information.
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1. model name:{{name}}
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2. model description:{{description}}
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3. model formulation:{{formulation}}
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4. model variables:{{variables}}.
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You must complete the forward function as far as you can do.
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\# Execution
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Your implemented code will be exectued in the follow way
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{{execute_desc}}
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