Repository: nilearn/nilearn

Issue:
fetch_abide_pcp docstring does not describe return value
<!--Describe your proposed enhancement in detail.-->

<!--List any pages that would be impacted by the enhancement.-->
### Affected pages

reference for this function: https://nilearn.github.io/stable/modules/generated/nilearn.datasets.fetch_abide_pcp.html#nilearn.datasets.fetch_abide_pcp



Discussion:

Resolving patch:
diff --git a/nilearn/datasets/func.py b/nilearn/datasets/func.py
--- a/nilearn/datasets/func.py
+++ b/nilearn/datasets/func.py
@@ -1087,10 +1087,79 @@ def fetch_abide_pcp(
     HANDEDNESS_SCORE : integer in [-100, 100], optional
         Positive = Right, Negative = Left, 0 = Ambidextrous.
 
+    Returns
+    -------
+    data : :class:`sklearn.utils.Bunch`
+        Dictionary-like object, the keys are described below.
+
+    - 'description': :obj:`str`, description of the dataset.
+
+    - 'phenotypic': :obj:`pandas.DataFrame`
+      phenotypic information for each subject.
+
+    - Specific Derivative Keys:
+      Additional keys,'func_preproc' being the default, are
+      introduced based on the provided 'derivatives'
+      parameter during fetching. Any combination of the
+      parameters below may occur.
+
+        - 'func_preproc' (default): :obj:`numpy.ndarray`,
+          paths to preprocessed functional MRI data in NIfTI format.
+          This key is present by default when fetching the dataset.
+        - 'alff': :obj:`numpy.ndarray`,
+          amplitude values of low-frequency fluctuations
+          in functional MRI data.
+        - 'degree_binarize': :obj:`numpy.ndarray`,
+          data specific to binarized node degree in brain networks.
+        - 'degree_weighted': :obj:`numpy.ndarray`,
+          data specific to weighted node degree,
+          considering connectivity strength in brain networks.
+        - 'dual_regression': :obj:`numpy.ndarray`,
+          results from dual regression analysis,
+          often involving the identification of resting-state networks.
+        - 'eigenvector_binarize': :obj:`numpy.ndarray`,
+          data specific to binarized eigenvector
+          centrality, a measure of node influence in brain networks.
+        - 'eigenvector_weighted': :obj:`numpy.ndarray`,
+          data specific to weighted eigenvector
+          centrality, reflecting node influence with consideration
+          of connectivity strength.
+        - 'falff': :obj:`numpy.ndarray`,
+          data specific to fractional amplitude values of
+          low-frequency fluctuations.
+        - 'func_mask': :obj:`numpy.ndarray`,
+          functional mask data, often used to define regions of interest.
+        - 'func_mean': :obj:`numpy.ndarray`,
+          mean functional MRI data,
+          representing average activity across the brain.
+        - 'lfcd': :obj:`numpy.ndarray`,
+          data specific to local functional connectivity density
+          in brain networks.
+        - 'reho': :obj:`numpy.ndarray`,
+          data specific to regional homogeneity in functional MRI data.
+        - 'rois_aal': :obj:`numpy.ndarray`,
+          data specific to anatomical regions
+          defined by the Automatic Anatomical Labeling atlas.
+        - 'rois_cc200': :obj:`numpy.ndarray`
+          data specific to regions defined by the Craddock 200 atlas.
+        - 'rois_cc400': :obj:`numpy.ndarray`,
+          data specific to regions defined by the Craddock 400 atlas.
+        - 'rois_dosenbach160': :obj:`numpy.ndarray`,
+          data specific to regions defined by the Dosenbach 160 atlas.
+        - 'rois_ez': :obj:`numpy.ndarray`,
+          data specific to regions defined by the EZ atlas.
+        - 'rois_ho': :obj:`numpy.ndarray`,
+          data specific to regions defined by the Harvard-Oxford atlas.
+        - 'rois_tt': :obj:`numpy.ndarray`,
+          data specific to regions defined by the Talairach atlas.
+        - 'vmhc': :obj:`numpy.ndarray`,
+          data specific to voxel-mirrored homotopic connectivity in
+          functional MRI data.
+
     Notes
     -----
     Code and description of preprocessing pipelines are provided on the
-    `PCP website <http://preprocessed-connectomes-project.github.io/>`.
+    `PCP website <http://preprocessed-connectomes-project.github.io/>`_.
 
     References
     ----------


Test patch: