You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: chnsScaling.m
    path of file: ./repos/toolbox/channels
    
    the code of the file until where you have to start completion: function [lambdas,as,scales,fs] = chnsScaling( pChns, Is, show )
% Compute lambdas for channel power law scaling.
%
% For a broad family of features, including gradient histograms and all
% channel types tested, the feature responses computed at a single scale
% can be used to approximate feature responses at nearby scales. The
% approximation is accurate at least within an entire scale octave. For
% details and to understand why this unexpected result holds, please see:
%   P. Dollár, R. Appel, S. Belongie and P. Perona
%   "Fast Feature Pyramids for Object Detection", PAMI 2014.
%
% This function computes channels at multiple image scales and plots the
% resulting power law scaling. The purpose of this function is two-fold:
% (1) compute lambdas for fast approximate channel computation for use in
% chnsPyramid() and (2) provide a visualization of the power law channel

    
function [lambdas,as,scales,fs] = chnsScaling( pChns, Is, show )
% Compute lambdas for channel power law scaling.
%
% For a broad family of features, including gradient histograms and all
% channel types tested, the feature responses computed at a single scale
% can be used to approximate feature responses at nearby scales. The
% approximation is accurate at least within an entire scale octave. For
% details and to understand why this unexpected result holds, please see:
%   P. Dollár, R. Appel, S. Belongie and P. Perona
%   "Fast Feature Pyramids for Object Detection", PAMI 2014.
%
% This function computes channels at multiple image scales and plots the
% resulting power law scaling. The purpose of this function is two-fold:
% (1) compute lambdas for fast approximate channel computation for use in
% chnsPyramid() and (2) provide a visualization of the power law channel
% scaling described in the BMVC2010 paper.
%
% chnsScaling() takes two main inputs: the parameters for computing image
% channels (pChns), and an image or set of images (Is). The images are
% cropped to the dimension of the smallest image for simplicity of
% computing the lambdas (and fairly high resolution images are best). The
% computed lambdas will depend