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Fix softmax documentation #4824

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Jun 20, 2022
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Fix softmax documentation #4824

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925cd0f
fix softmax doc
rusty1s Jun 20, 2022
be6ad0a
changelog
rusty1s Jun 20, 2022
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2 changes: 1 addition & 1 deletion CHANGELOG.md
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Expand Up @@ -48,7 +48,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
- Refactored reading molecular positions from sdf file for qm9 datasets ([4654](https://github.com/pyg-team/pytorch_geometric/pull/4654))
- Fixed `MLP.jittable()` bug in case `return_emb=True` ([#4645](https://github.com/pyg-team/pytorch_geometric/pull/4645), [#4648](https://github.com/pyg-team/pytorch_geometric/pull/4648))
- The generated node features of `StochasticBlockModelDataset` are now ordered with respect to their labels ([#4617](https://github.com/pyg-team/pytorch_geometric/pull/4617))
- Removed unnecessary colons and fixed typos in the documentation ([#4616](https://github.com/pyg-team/pytorch_geometric/pull/4616))
- Fixed typos in the documentation ([#4616](https://github.com/pyg-team/pytorch_geometric/pull/4616), [#4824](https://github.com/pyg-team/pytorch_geometric/pull/4824))
- The `bias` argument in `TAGConv` is now actually applied ([#4597](https://github.com/pyg-team/pytorch_geometric/pull/4597))
- Fixed subclass behaviour of `process` and `download` in `Datsaet` ([#4586](https://github.com/pyg-team/pytorch_geometric/pull/4586))
- Fixed filtering of attributes for loaders in case `__cat_dim__ != 0` ([#4629](https://github.com/pyg-team/pytorch_geometric/pull/4629))
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1 change: 0 additions & 1 deletion docs/source/modules/utils.rst
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Expand Up @@ -11,4 +11,3 @@ torch_geometric.utils

.. automodule:: torch_geometric.utils
:members:
:undoc-members:
12 changes: 7 additions & 5 deletions torch_geometric/utils/softmax.py
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@@ -1,16 +1,18 @@
from typing import Optional

import torch
from torch import Tensor
from torch_scatter import gather_csr, scatter, segment_csr

from .num_nodes import maybe_num_nodes


@torch.jit.script
def softmax(src: Tensor, index: Optional[Tensor] = None,
ptr: Optional[Tensor] = None, num_nodes: Optional[int] = None,
dim: int = 0) -> Tensor:
def softmax(
src: Tensor,
index: Optional[Tensor] = None,
ptr: Optional[Tensor] = None,
num_nodes: Optional[int] = None,
dim: int = 0,
) -> Tensor:
r"""Computes a sparsely evaluated softmax.
Given a value tensor :attr:`src`, this function first groups the values
along the first dimension based on the indices specified in :attr:`index`,
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