TypeError: kernel_initializer keyword not understood when building a dense layer.

[decisivesacha]

Here is the output:
Using MXNet backend.
Traceback (most recent call last):
File "run.py", line 128, in
model = Model(config_args['alpha'], config_args['gamma'], config_args['input_size'], config_args['hidden_size'])
File "/tmp/Model.py", line 37, in init
kernel_initializer='glorot_normal'))
File "/usr/local/lib/python3.6/dist-packages/Keras-1.2.2-py3.6.egg/keras/layers/core.py", line 785, in init
super(Dense, self).init(**kwargs)
File "/usr/local/lib/python3.6/dist-packages/Keras-1.2.2-py3.6.egg/keras/engine/topology.py", line 326, in init
raise TypeError('Keyword argument not understood:', kwarg)
TypeError: ('Keyword argument not understood:', 'kernel_initializer')

The same issue comes up for use_bias = False.

Not sure why this is the case.
I'm using the master version from this repo. Version 1.2.2 with python 3.67 and mxnet 1.3.1

[decisivesacha]

keras.clone_model does not seem to have been ported over either as this is throwing an import error.