[Python] Problem possibly with np.apply_along_axis?

I am working with the Fashion MNIST data and I am trying to use np.apply_along_axis. I think this is were my issue is. I have tried it with a toy data set of 6-(1,2) points for training and 2-(,2) test points and a 100 point subset of fashion MNIST data with 1 test point and it appears to work. So when I tried it with the more than 2 data points it does not work. I have posted my code and the error below. If anyone could give me any pointers I would greatly appreciate it. I deleted the inside part of the matrices in the error code to make this post shorter, in case anyone is wondering, just left the first and last few lines.

import tensorflow as tf
from tensorflow import keras

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy import io

from scipy.spatial import distance

fashion_mnist = keras.datasets.fashion_mnist

from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix

from sklearn.neighbors import NearestNeighbors

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

X1=train_images.flatten().reshape((60000,784))
x1=test_images.flatten().reshape((10000,784))

# Subsetting fashion MNIST to work with

X=X1[0:100,:]
Y=train_labels[0:100]
x=x1[0:3,:]
y=test_labels[0:3]

def CLC(target):
centroids=[]
# Find k nearest neighbors by label
for i in list(labels):
X_i = X[Y==i]
# print(X_i)
Y_i = Y[Y==i]
nbrs_i = NearestNeighbors(n_neighbors=1).fit(X_i)
distances, indices = nbrs_i.kneighbors(target.reshape(1,-1))

# Subsetting data for nearest neighbors
xss=X_i[indices,:]
yss=Y_i[indices]
# Centroid of k nearest neighbors by label
# print(xss)
# print(yss)
centroid = np.mean(xss, axis=1)
print(centroid)
centroids.append(centroid)
#centroids
# find closest local centroid
centroids=np.squeeze(centroids)
clf = NearestCentroid()
clf.fit(centroids, labels)
return(clf.predict(target))

y_pred=np.apply_along_axis(CLC, axis=1, arr=x)
#acc=sum(y_pred==y)/len(y_pred)
#acc


Error Output:

[[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 122. 101. 73. 59.
39. 108. 89. 91. 83. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 3. 215. 232. 236. 223.


0. 0. 0. 61. 81. 91. 56. 12. 99. 75. 78. 75. 99. 70.
61. 70. 77. 77. 61. 58. 89. 7. 55. 188. 97. 78. 0. 0.
0. 0. 0. 9. 28. 23. 4. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 1. 66. 33. 11. 0. 0.]]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-569-bda921e18e4e> in <module>
26 return(clf.predict(target))
27
---> 28 y_pred=np.apply_along_axis(CLC, axis=1, arr=x)
29 #acc=sum(y_pred==y)/len(y_pred)
30 #acc

<__array_function__ internals> in apply_along_axis(*args, **kwargs)

~/opt/anaconda3/lib/python3.7/site-packages/numpy/lib/shape_base.py in apply_along_axis(func1d, axis, arr, *args, **kwargs)
377 except StopIteration:
378 raise ValueError('Cannot apply_along_axis when any iteration dimensions are 0')
--> 379 res = asanyarray(func1d(inarr_view[ind0], *args, **kwargs))
380
381 # build a buffer for storing evaluations of func1d.

<ipython-input-569-bda921e18e4e> in CLC(target)
24 clf = NearestCentroid()
25 clf.fit(centroids, labels)
---> 26 return(clf.predict(target))
27
28 y_pred=np.apply_along_axis(CLC, axis=1, arr=x)

~/opt/anaconda3/lib/python3.7/site-packages/sklearn/neighbors/nearest_centroid.py in predict(self, X)
190 check_is_fitted(self, 'centroids_')
191
--> 192 X = check_array(X, accept_sparse='csr')
193 return self.classes_[pairwise_distances(
194 X, self.centroids_, metric=self.metric).argmin(axis=1)]

~/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
519 "Reshape your data either using array.reshape(-1, 1) if "
520 "your data has a single feature or array.reshape(1, -1) "
--> 521 "if it contains a single sample.".format(array))
522
523 # in the future np.flexible dtypes will be handled like object dtypes

ValueError: Expected 2D array, got 1D array instead:
array=[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

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