提交 b93bb585 authored 作者: Frédéric Bastien's avatar Frédéric Bastien

Add space for pep8 in documentation

上级 334d288d
.. currentmodule:: tensor .. currentmodule:: tensor
.. _libdoc_basic_tensor: .. _libdoc_basic_tensor:
...@@ -548,13 +547,13 @@ dimensions, see :meth:`_tensor_py_operators.dimshuffle`. ...@@ -548,13 +547,13 @@ dimensions, see :meth:`_tensor_py_operators.dimshuffle`.
.. function:: unbroadcast(x, *axes) .. function:: unbroadcast(x, *axes)
Make `x` impossible to broadcast in the specified axes `axes`. For Make `x` impossible to broadcast in the specified axes `axes`. For
example, `unbroadcast(x,0)` will make the first dimension of `x` example, `unbroadcast(x, 0)` will make the first dimension of `x`
unbroadcastable. unbroadcastable.
.. function:: addbroadcast(x, *axes) .. function:: addbroadcast(x, *axes)
Make `x` broadcastable in the specified axes `axes`. For Make `x` broadcastable in the specified axes `axes`. For
example, `addbroadcast(x,0)` will make the first dimension of `x` example, `addbroadcast(x, 0)` will make the first dimension of `x`
broadcastable. When performing the function, if the length of `x` broadcastable. When performing the function, if the length of `x`
along that dimension is not 1, a ``ValueError`` will be raised. along that dimension is not 1, a ``ValueError`` will be raised.
...@@ -573,7 +572,7 @@ dimensions, see :meth:`_tensor_py_operators.dimshuffle`. ...@@ -573,7 +572,7 @@ dimensions, see :meth:`_tensor_py_operators.dimshuffle`.
dimensions, but with all remaining dimensions of `x` collapsed into dimensions, but with all remaining dimensions of `x` collapsed into
the last dimension. the last dimension.
For example, if we flatten a tensor of shape (2,3,4,5) with flatten(x, For example, if we flatten a tensor of shape (2, 3, 4, 5) with flatten(x,
outdim=2), then we'll have the same (2-1=1) leading dimensions (2,), and the outdim=2), then we'll have the same (2-1=1) leading dimensions (2,), and the
remaining dimensions are collapsed. So the output in this example would remaining dimensions are collapsed. So the output in this example would
have shape (2, 60). have shape (2, 60).
......
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