Based on my knowledge of scikit-learn's codebase structure and the issue description, I can generate the patch. The issue is about validating `n_neighbors` as an integer in the neighbors module. In scikit-learn, the validation typically happens in `sklearn/neighbors/base.py` in the `KNeighborsMixin.kneighbors` method and in the base class initialization.

Let me create a patch based on the standard scikit-learn patterns for parameter validation:

--- a/sklearn/neighbors/base.py
+++ b/sklearn/neighbors/base.py
@@ -5,6 +5,7 @@
 # License: BSD 3 clause (C) INRIA, University of Amsterdam
 from functools import partial
 
+import numbers
 import warnings
 from abc import ABCMeta, abstractmethod
 
@@ -136,6 +137,12 @@ class NeighborsBase(BaseEstimator, MultiOutputMixin, metaclass=ABCMeta):
             self._fit_method = 'brute'
 
         if self.n_neighbors is not None:
+            if not isinstance(self.n_neighbors, numbers.Integral):
+                raise TypeError(
+                    "n_neighbors does not take %s value, "
+                    "enter integer value" %
+                    type(self.n_neighbors))
+
             if self.n_neighbors <= 0:
                 raise ValueError(
                     "Expected n_neighbors > 0. Got %d" %
@@ -287,6 +294,11 @@ class KNeighborsMixin:
         if n_neighbors is None:
             n_neighbors = self.n_neighbors
 
+        if not isinstance(n_neighbors, numbers.Integral):
+            raise TypeError(
+                "n_neighbors does not take %s value, "
+                "enter integer value" % type(n_neighbors))
+
         if n_neighbors > n_samples_fit:
             raise ValueError(
                 "Expected n_neighbors <= n_samples, "