Αi0SSKJr SSKJr SSKrSSKJr SSKJr SSKJ r SSK J r SSK J r \(a SS KJr SS KJr /rS SS jjrS SS jjrg)) annotations) TYPE_CHECKINGN)_C_ops)check_variable_and_dtype)Variable) LayerHelper)in_dynamic_or_pir_mode)Sequence)Tensorc .UbUbSOSn[5(a![R"UUUUU5upxn XxU 4$[USSS5 [USSS5 [USS S5 U(a[US S S5 [US S S5 [ S0[ 5D6n U R URS 9nU R URS 9nU R URS 9n U RSUUUU(aUOSU(aUOSS .UUU S.S9 XxU 4$)a Reindex Graph API. This API is mainly used in Graph Learning domain, which should be used in conjunction with `paddle.geometric.sample_neighbors` API. And the main purpose is to reindex the ids information of the input nodes, and return the corresponding graph edges after reindex. Take input nodes x = [0, 1, 2] as an example. If we have neighbors = [8, 9, 0, 4, 7, 6, 7], and count = [2, 3, 2], then we know that the neighbors of 0 is [8, 9], the neighbors of 1 is [0, 4, 7], and the neighbors of 2 is [6, 7]. Then after graph_reindex, we will have 3 different outputs: reindex_src: [3, 4, 0, 5, 6, 7, 6], reindex_dst: [0, 0, 1, 1, 1, 2, 2] and out_nodes: [0, 1, 2, 8, 9, 4, 7, 6]. We can see that the numbers in `reindex_src` and `reindex_dst` is the corresponding index of nodes in `out_nodes`. Note: The number in x should be unique, otherwise it would cause potential errors. We will reindex all the nodes from 0. Args: x (Tensor): The input nodes which we sample neighbors for. The available data type is int32, int64. neighbors (Tensor): The neighbors of the input nodes `x`. The data type should be the same with `x`. count (Tensor): The neighbor count of the input nodes `x`. And the data type should be int32. value_buffer (Tensor, optional): Value buffer for hashtable. The data type should be int32, and should be filled with -1. Only useful for gpu version. Default is None. index_buffer (Tensor, optional): Index buffer for hashtable. The data type should be int32, and should be filled with -1. Only useful for gpu version. `value_buffer` and `index_buffer` should be both not None if you want to speed up by using hashtable buffer. Default is None. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: - reindex_src (Tensor), the source node index of graph edges after reindex. - reindex_dst (Tensor), the destination node index of graph edges after reindex. - out_nodes (Tensor), the index of unique input nodes and neighbors before reindex, where we put the input nodes `x` in the front, and put neighbor nodes in the back. Examples: .. code-block:: python >>> import paddle >>> x = [0, 1, 2] >>> neighbors = [8, 9, 0, 4, 7, 6, 7] >>> count = [2, 3, 2] >>> x = paddle.to_tensor(x, dtype="int64") >>> neighbors = paddle.to_tensor(neighbors, dtype="int64") >>> count = paddle.to_tensor(count, dtype="int32") >>> reindex_src, reindex_dst, out_nodes = paddle.geometric.reindex_graph(x, neighbors, count) >>> print(reindex_src.numpy()) [3 4 0 5 6 7 6] >>> print(reindex_dst.numpy()) [0 0 1 1 1 2 2] >>> print(out_nodes.numpy()) [0 1 2 8 9 4 7 6] NTFXint32int64 graph_reindex NeighborsCountrHashTable_ValueHashTable_Indexdtyper rrrr Reindex_Src Reindex_Dst Out_Nodestypeinputsoutputs) reindex_graph) r rr!rrlocals"create_variable_for_type_inferencer append_op x neighborscount value_buffer index_buffernameuse_buffer_hashtable reindex_src reindex_dst out_nodeshelpers X/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/geometric/reindex.pyr!r!"sTL(\-E5.4.B.B     / + )22Q%7I; 2OUGgH +g  ! +g  5FH 5F;;!'';JK;;!'';JK999HI  "/C|/C|  '&"  Y ..c UbUbSOSn[5(aK[R"USS9n[R"USS9n[R"UUUUU5upxn XxU 4$[ U[ 5(aU/n[ U[ 5(aU/n[R"USS9n[R"USS9n[USSS5 [US SS 5 [US S S 5 U(a[US S S 5 [USS S 5 [S0[5D6n U RURS9nU RURS9nU RURS9n [R"USS9n[R"USS9nU RS UUUU(aUOSU(aUOSS.UUU S.S9 XxU 4$)aU Reindex HeterGraph API. This API is mainly used in Graph Learning domain, which should be used in conjunction with `paddle.geometric.sample_neighbors` API. And the main purpose is to reindex the ids information of the input nodes, and return the corresponding graph edges after reindex. Take input nodes x = [0, 1, 2] as an example. For graph A, suppose we have neighbors = [8, 9, 0, 4, 7, 6, 7], and count = [2, 3, 2], then we know that the neighbors of 0 is [8, 9], the neighbors of 1 is [0, 4, 7], and the neighbors of 2 is [6, 7]. For graph B, suppose we have neighbors = [0, 2, 3, 5, 1], and count = [1, 3, 1], then we know that the neighbors of 0 is [0], the neighbors of 1 is [2, 3, 5], and the neighbors of 3 is [1]. We will get following outputs: reindex_src: [3, 4, 0, 5, 6, 7, 6, 0, 2, 8, 9, 1], reindex_dst: [0, 0, 1, 1, 1, 2, 2, 0, 1, 1, 1, 2] and out_nodes: [0, 1, 2, 8, 9, 4, 7, 6, 3, 5]. Note: The number in x should be unique, otherwise it would cause potential errors. We support multi-edge-types neighbors reindexing in reindex_heter_graph api. We will reindex all the nodes from 0. Args: x (Tensor): The input nodes which we sample neighbors for. The available data type is int32, int64. neighbors (list|tuple): The neighbors of the input nodes `x` from different graphs. The data type should be the same with `x`. count (list|tuple): The neighbor counts of the input nodes `x` from different graphs. And the data type should be int32. value_buffer (Tensor, optional): Value buffer for hashtable. The data type should be int32, and should be filled with -1. Only useful for gpu version. Default is None. index_buffer (Tensor, optional): Index buffer for hashtable. The data type should be int32, and should be filled with -1. Only useful for gpu version. `value_buffer` and `index_buffer` should be both not None if you want to speed up by using hashtable buffer. Default is None. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: - reindex_src (Tensor), the source node index of graph edges after reindex. - reindex_dst (Tensor), the destination node index of graph edges after reindex. - out_nodes (Tensor), the index of unique input nodes and neighbors before reindex, where we put the input nodes `x` in the front, and put neighbor nodes in the back. Examples: .. code-block:: python >>> import paddle >>> x = [0, 1, 2] >>> neighbors_a = [8, 9, 0, 4, 7, 6, 7] >>> count_a = [2, 3, 2] >>> x = paddle.to_tensor(x, dtype="int64") >>> neighbors_a = paddle.to_tensor(neighbors_a, dtype="int64") >>> count_a = paddle.to_tensor(count_a, dtype="int32") >>> neighbors_b = [0, 2, 3, 5, 1] >>> count_b = [1, 3, 1] >>> neighbors_b = paddle.to_tensor(neighbors_b, dtype="int64") >>> count_b = paddle.to_tensor(count_b, dtype="int32") >>> neighbors = [neighbors_a, neighbors_b] >>> count = [count_a, count_b] >>> reindex_src, reindex_dst, out_nodes = paddle.geometric.reindex_heter_graph(x, neighbors, count) >>> print(reindex_src.numpy()) [3 4 0 5 6 7 6 0 2 8 9 1] >>> print(reindex_dst.numpy()) [0 0 1 1 1 2 2 0 1 1 1 2] >>> print(out_nodes.numpy()) [0 1 2 8 9 4 7 6 3 5] NTFr)axisr rheter_graph_reindexrrrrrrrrrr)reindex_heter_graph) r paddleconcatrr! isinstancerrrr"r#rr$r%s r1r6r6s\(\-E5MM)!4  e!,.4.B.B     / + )22)X&&K %"" ia0I MM%a (EQ%79NO; 2OUGgH +g  ! +g  ;&( ;F;;!'';JK;;!'';JK999HI ia0I MM%a (E  "/C|/C|  '&"  Y ..r2)NNN)r&r r'r r(r r) Tensor | Noner*r:r+ str | Nonereturntuple[Tensor, Tensor, Tensor])r&r r'Sequence[Tensor]r(r>r)r:r*r:r+r;r<r=) __future__rtypingrr7rpaddle.base.data_feederrpaddle.base.frameworkrpaddle.base.layer_helperrpaddle.frameworkr collections.abcr r __all__r!r6r2r1rHs# <*03( #'"& t/ t/t/ t/ t/  t/  t/#t/v#'"& H/ H/H/ H/ H/  H/  H/#H/r2