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       differ by exactly one bit

    >>> gray_code(2)
    [0, 1, 3, 2]

    >>> gray_code(1)
    [0, 1]

    >>> gray_code(3)
    [0, 1, 3, 2, 6, 7, 5, 4]

    >>> gray_code(-1)
    Traceback (most recent call last):
        ...
    ValueError: The given input must be positive

    >>> gray_code(10.6)
    Traceback (most recent call last):
        ...

"""Wrapper that converts a color observation to grayscale."""
import numpy as np

import gym
from gym.spaces import Box


class GrayScaleObservation(gym.ObservationWrapper):
    """Convert the image observation from RGB to gray scale.

    Example:
        >>> env = gym.make('CarRacing-v1')
        >>> env.observation_space
        Box(0, 255, (96, 96, 3), uint8)
        >>> env = GrayScaleObservation(gym.make('CarRacing-v1'))

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# Gray Box Testing

Gray box testing is a software testing technique to test a software product or application with partial knowledge of the internal structure of the application. The purpose of gray box testing is to search and identify the defects due to improper code structure or improper use of applications.

Visit the following resources to learn more:

# Gray Box Testing

Gray box testing is a software testing technique to test a software product or application with partial knowledge of the internal structure of the application. The purpose of gray box testing is to search and identify the defects due to improper code structure or improper use of applications.

Visit the following resources to learn more:

# Gray Box Testing

Gray box testing is a software testing technique to test a software product or application with partial knowledge of the internal structure of the application. The purpose of gray box testing is to search and identify the defects due to improper code structure or improper use of applications.

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