pylibcugraphops.operators.agg_concat_n2n_fwd = <nanobind.nb_func object>#
Computes the forward pass for simple aggregation using node features in an

node-to-node reduction (n2n) on a graph while concatenating the original features of output at the end (agg_concat).

    output_embedding: device array, input_embedding: device array,
    graph: pylibcugraphops.csc_int[32|64],
    aggregation_operation: pylibcugraphops.operators.AggOp = pylibcugraphops.operators.AggOp.Sum,
    output_extrema_location: Optional[device array] = None, stream_id: int = 0
) -> None
output_embeddingdevice array type

Device array containing the output node embeddings. Shape: (graph.n_dst_nodes, 2 * dim_in).

input_embeddingdevice array type

Device array containing the input node embeddings. Shape: (graph.n_dst_nodes, dim_in).

graphopaque graph type

The graph used for the operation.

aggregation_operationAggOp, default=AggOp.Sum

The kind of aggregation operation.

output_extrema_locationdevice array type | None

Device array containing the location of the min/max embeddings. This is required for min/max aggregation only, and can be None otherwise. Shape: (graph.n_dst_nodes, dim_in) if set.

stream_idint, default=0

CUDA stream pointer as a python int.