I pointed to the function finite_difference_derivative when applied to an array input.

In the docs the following list of headers exists:

scalar derivatives
"multi point derivatives"
gradients
jacobians
hessians

The term derivative usually applies to a function $f: R \to R$, or more generally $R \to R^m$, but in any case it takes a scalar input point.
Reading "multi point derivative" I imagine we have to supply several input points $x^{(1)},x^{(2)},x^{(3)}$ to this routine, and we get several derivatives, one at each point $f'(x^{(1)}),f'(x^{(2)}),f'(x^{(3)})$.
The source code gives me confirmation of that, but for lack of docstrings I was a bit confused when only reading the docs.

FiniteDiff.jl/src/derivatives.jl

Lines 71 to 74 in be7777f

    
           #= 
        
           Compute the derivative df of a scalar-valued map f at a collection of points x. 
        
           =# 
        
           function finite_difference_derivative(

	#=
	Compute the derivative df of a scalar-valued map f at a collection of points x.
	=#
	function finite_difference_derivative(

What does "multi point derivatives" mean? #181

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