DOC Fix dropdown content indentation (scikit-learn#30116)

Author: abhi-jha

doc/modules/manifold.rst

@@ -293,24 +293,24 @@ It requires ``n_neighbors > n_components * (n_components + 3) / 2``.
 
 .. dropdown:: Complexity
 
-The HLLE algorithm comprises three stages:
+  The HLLE algorithm comprises three stages:
 
-1. **Nearest Neighbors Search**.  Same as standard LLE
+  1. **Nearest Neighbors Search**.  Same as standard LLE
 
-2. **Weight Matrix Construction**. Approximately
-   :math:`O[D N k^3] + O[N d^6]`.  The first term reflects a similar
-   cost to that of standard LLE.  The second term comes from a QR
-   decomposition of the local hessian estimator.
+  2. **Weight Matrix Construction**. Approximately
+     :math:`O[D N k^3] + O[N d^6]`.  The first term reflects a similar
+     cost to that of standard LLE.  The second term comes from a QR
+     decomposition of the local hessian estimator.
 
-3. **Partial Eigenvalue Decomposition**. Same as standard LLE.
+  3. **Partial Eigenvalue Decomposition**. Same as standard LLE.
 
-The overall complexity of standard HLLE is
-:math:`O[D \log(k) N \log(N)] + O[D N k^3] + O[N d^6] + O[d N^2]`.
+  The overall complexity of standard HLLE is
+  :math:`O[D \log(k) N \log(N)] + O[D N k^3] + O[N d^6] + O[d N^2]`.
 
-* :math:`N` : number of training data points
-* :math:`D` : input dimension
-* :math:`k` : number of nearest neighbors
-* :math:`d` : output dimension
+  * :math:`N` : number of training data points
+  * :math:`D` : input dimension
+  * :math:`k` : number of nearest neighbors
+  * :math:`d` : output dimension
 
 .. rubric:: References
 

sklearn/manifold/tests/test_spectral_embedding.py

@@ -54,7 +54,7 @@
 
 def _assert_equal_with_sign_flipping(A, B, tol=0.0):
     """Check array A and B are equal with possible sign flipping on
-    each columns"""
+    each column"""
     tol_squared = tol**2
     for A_col, B_col in zip(A.T, B.T):
         assert (