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import torch as T
import numpy as NP

### Norm of vector difference

# Checker
def normdiff_assert(X, Y, normdiff):
    norm = normdiff(X, Y)
    for i in range(X.size()[2]):

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import argparse
import mxnet as mx
from mxnet import nn
from mxnet.contrib import autograd
import numpy as np
import time
import os

ngpu = 1
nz = 100

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# Modified from example/autograd/{data,dcgan}.py to make it standalone.
import argparse
import mxnet as mx
from mxnet import nn
from mxnet.contrib import autograd
import numpy as np
import matplotlib.pyplot as PL
import time
import os

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#!/usr/bin/env python

"""
Code to load an expert policy and generate roll-out data for behavioral cloning.
Example usage:
    python run_expert.py experts/Humanoid-v1.pkl Humanoid-v1 --render \
            --num_rollouts 20

Author of this script and included expert policies: Jonathan Ho (hoj@openai.com)
"""

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import tensorflow as TF
import modeltf as model

import numpy as NP
import numpy.random as RNG

import h5py

import argparse

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'''
Original implementation
https://github.com/clab/dynet_tutorial_examples/blob/master/tutorial_parser.ipynb

The code structure and variable names are similar for better reference.

Not for serious business, just for some comparison between PyTorch and DyNet
(and I still prefer PyTorch)
'''
import torch as T