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DS-1000 / 436 / Input example:

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Problem:
Input example:
I have a numpy array, e.g.
a=np.array([[0,1], [2, 1], [4, 8]])
Desired output:
I would like to produce a mask array with the max value along a given axis, in my case axis 1, being True and all others being False. e.g. in this case
mask = np.array([[False, True], [True, False], [False, True]])
Attempt:
I have tried approaches using np.amax but this returns the max values in a flattened list:
>>> np.amax(a, axis=1)
array([1, 2, 8])
and np.argmax similarly returns the indices of the max values along that axis.
>>> np.argmax(a, axis=1)
array([1, 0, 1])
I could iterate over this in some way but once these arrays become bigger I want the solution to remain something native in numpy.
A:
<code>
import numpy as np
a = np.array([[0, 1], [2, 1], [4, 8]])
</code>
mask = ... # put solution in this variable
BEGIN SOLUTION
<code>

library

Numpy

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Source and history

upstream_id / Original problem ID
436
task_id
cea2724c-6bc3-56be-b5ba-25a870e4bd4d
task_key
default--test--436
task_revision_id
1
config / split
default / test

Official source

initial import