ó Α®iÏ%ãóâ•SSKJr SSKJr SSKJr SSKJr SSKJ r SSK J r \(aSSKJ r /r S SS jjrS SS jjrS SS jjrS SS jjrg)é)Ú annotations)Ú TYPE_CHECKING)Ú_C_ops)Úcheck_variable_and_dtype)Ú LayerHelper)Úin_dynamic_or_pir_mode)ÚTensorNcóD•[5(a[R"XS5$[USSS5 [USSS5 [ S 0[ 5D6nUR URS9nUR URS9nURSXS.XES .S S0S 9 U$) aE Segment Sum Operator. This operator sums the elements of input `data` which with the same index in `segment_ids`. It computes a tensor such that $out_i = \\sum_{j} data_{j}$ where sum is over j such that `segment_ids[j] == i`. Args: data (Tensor): A tensor, available data type float32, float64, int32, int64, float16. segment_ids (Tensor): A 1-D tensor, which have the same size with the first dimension of input data. Available data type is int32, int64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: - output (Tensor), the reduced result. Examples: .. code-block:: python >>> import paddle >>> data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32') >>> segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32') >>> out = paddle.geometric.segment_sum(data, segment_ids) >>> print(out.numpy()) [[4. 4. 4.] [4. 5. 6.]] ÚSUMÚX©Úfloat32Úfloat64Úint32Úint64Úfloat16Úuint16Ú segment_poolÚ SegmentIds©rr©Údtype©r r©ÚOutÚ SummedIdsÚpooltype©ÚtypeÚinputsÚoutputsÚattrs)Ú segment_sum© rrrrrÚlocalsÚ"create_variable_for_type_inferencerÚ append_op©ÚdataÚ segment_idsÚnameÚhelperÚoutÚ summed_idss ÚU/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/geometric/math.pyr#r#s¹€ôD×ÑÜ×"Ò" 4°eÓ<Ð<ä Ø Ø Ø IØ ô  ô !Ø ˜Ð'9¸>ô ôÑ7¬f«hÑ7ˆØ×7Ñ7¸d¿j¹jÐ7ÐIˆØ×>Ñ>ÀTÇZÁZÐ>ÐPˆ Ø×ÑØØÑ9ØÑ9ؘuÐ%ð ñ ð ˆ ócóD•[5(a[R"XS5$[USSS5 [USSS5 [ S 0[ 5D6nUR URS9nUR URS9nURSXS.XES .S S0S 9 U$) a˜ Segment mean Operator. This operator calculate the mean value of input `data` which with the same index in `segment_ids`. It computes a tensor such that $out_i = \\frac{1}{n_i} \\sum_{j} data[j]$ where sum is over j such that 'segment_ids[j] == i' and $n_i$ is the number of all index 'segment_ids[j] == i'. Args: data (tensor): a tensor, available data type float32, float64, int32, int64, float16. segment_ids (tensor): a 1-d tensor, which have the same size with the first dimension of input data. available data type is int32, int64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: - output (Tensor), the reduced result. Examples: .. code-block:: python >>> import paddle >>> data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32') >>> segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32') >>> out = paddle.geometric.segment_mean(data, segment_ids) >>> print(out.numpy()) [[2. 2. 2.] [4. 5. 6.]] ÚMEANr r rrrrrrrr)Ú segment_meanr$r(s r/r3r3Xs¹€ôH×ÑÜ×"Ò" 4°fÓ=Ð=ä Ø Ø Ø IØ ô  ô !Ø ˜Ð'9¸>ô ôÑ8¬v«xÑ8ˆØ×7Ñ7¸d¿j¹jÐ7ÐIˆØ×>Ñ>ÀTÇZÁZÐ>ÐPˆ Ø×ÑØØÑ9ØÑ9ؘvÐ&ð ñ ð ˆ r0cóD•[5(a[R"XS5$[USSS5 [USSS5 [ S 0[ 5D6nUR URS9nUR URS9nURSXS.XES .S S0S 9 U$) aR Segment min operator. This operator calculate the minimum elements of input `data` which with the same index in `segment_ids`. It computes a tensor such that $out_i = \\min_{j} data_{j}$ where min is over j such that `segment_ids[j] == i`. Args: data (tensor): a tensor, available data type float32, float64, int32, int64, float16. segment_ids (tensor): a 1-d tensor, which have the same size with the first dimension of input data. available data type is int32, int64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: - output (Tensor), the reduced result. Examples: .. code-block:: python >>> import paddle >>> data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32') >>> segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32') >>> out = paddle.geometric.segment_min(data, segment_ids) >>> print(out.numpy()) [[1. 2. 1.] [4. 5. 6.]] ÚMINr r rrrrrrrr)Ú segment_minr$r(s r/r6r6•ó¹€ôF×ÑÜ×"Ò" 4°eÓ<Ð<ä Ø Ø Ø IØ ô  ô !Ø ˜Ð'9¸>ô ôÑ7¬f«hÑ7ˆØ×7Ñ7¸d¿j¹jÐ7ÐIˆØ×>Ñ>ÀTÇZÁZÐ>ÐPˆ Ø×ÑØØÑ9ØÑ9ؘuÐ%ð ñ ð ˆ r0cóD•[5(a[R"XS5$[USSS5 [USSS5 [ S 0[ 5D6nUR URS9nUR URS9nURSXS.XES .S S0S 9 U$) aR Segment max operator. This operator calculate the maximum elements of input `data` which with the same index in `segment_ids`. It computes a tensor such that $out_i = \\max_{j} data_{j}$ where max is over j such that `segment_ids[j] == i`. Args: data (tensor): a tensor, available data type float32, float64, int32, int64, float16. segment_ids (tensor): a 1-d tensor, which have the same size with the first dimension of input data. available data type is int32, int64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: - output (Tensor), the reduced result. Examples: .. code-block:: python >>> import paddle >>> data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32') >>> segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32') >>> out = paddle.geometric.segment_max(data, segment_ids) >>> print(out.numpy()) [[3. 2. 3.] [4. 5. 6.]] ÚMAXr r rrrrrrrr)Ú segment_maxr$r(s r/r:r:Ñr7r0)N)r)r r*r r+z str | NoneÚreturnr )Ú __future__rÚtypingrÚpaddlerÚpaddle.base.data_feederrÚpaddle.base.layer_helperrÚpaddle.frameworkrr Ú__all__r#r3r6r:©r0r/ÚrDsÏðõ#å åÝ<Ý0Ý3æÝà €ð;?ð8Ø ð8Ø%ð8Ø-7ð8à õ8ðx;?ð:Ø ð:Ø%ð:Ø-7ð:à õ:ð|;?ð9Ø ð9Ø%ð9Ø-7ð9à õ9ðz;?ð9Ø ð9Ø%ð9Ø-7ð9à ö9r0