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Need help solving the following problem, If anything can be used from the proofs from the Understanding Machine Learning: From Theory to Algorithms by Shai

Need help solving the following problem, If anything can be used from the proofs from the Understanding Machine Learning: From Theory to Algorithms by Shai & Shai it may be used, just please reference it
A decision list may be thought of as an ordered sequence of if-then-else statements. The sequence of conditions in the decision list is tested in order, and the answer associated with the first satisfied condition is output.
More formally, a k-decision list over the boolean variables x1,x2,dots,xn is an ordered sequence L={(c1,b1),(c2,b2),dots,(cl,bl)} and a bit b, in which each ci is a conjunction of at most k literals over x1,x2,dots,xn and each biin{0,1}. For any input ain{0,1}n, the value L(a) is defined to be bj where j is the smallest index satisfying cj(a)=1; if no such index exists, then L(a)=b. Thus, b is the "default" value in case a falls off the end of the list. We call bi the bit associated with the condition ci.
The next figure shows an example of a 2-decision list along with its evaluation on a particular input.
Figure 1: A 2-decision list and the path followed by an input. Evaluation starts at the leftmost item and continues to the right until the first condition is satisfied, at which point the binary value below becomes the final result of the evaluation.
Show that the VC dimension of 1-decision lists over {0,1}n is lower and upper bounded by linear functions, by showing that there exists ,,,inR such that:
*n+VCdim(H1-decisionlist)*n+
Hint: Show that 1-decision lists over {0,1}n compute linearly separable functions (halfspaces)
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