Confusing `ITensor(::ITensorNetwork)` behavior

The behavior of ITensor(::ITensorNetwork) is confusing, since it outputs a Vector{ITensor}:

julia> tn = randomITensorNetwork(named_grid((2, 2)); link_space=2)
ITensorNetwork{Tuple{Int64, Int64}} with 4 vertices:
4-element Vector{Tuple{Int64, Int64}}:
 (1, 1)
 (2, 1)
 (1, 2)
 (2, 2)

and 4 edge(s):
(1, 1) => (2, 1)
(1, 1) => (1, 2)
(2, 1) => (2, 2)
(1, 2) => (2, 2)

with vertex data:
4-element Dictionaries.Dictionary{Tuple{Int64, Int64}, Any}
 (1, 1) │ ((dim=2|id=727|"1×1↔2×1"), (dim=2|id=273|"1×1↔1×2"))
 (2, 1) │ ((dim=2|id=727|"1×1↔2×1"), (dim=2|id=845|"2×1↔2×2"))
 (1, 2) │ ((dim=2|id=273|"1×1↔1×2"), (dim=2|id=145|"1×2↔2×2"))
 (2, 2) │ ((dim=2|id=845|"2×1↔2×2"), (dim=2|id=145|"1×2↔2×2"))

julia> ITensor(tn)
4-element Vector{ITensor}:
 ITensor ord=2
Dim 1: (dim=2|id=727|"1×1↔2×1")
Dim 2: (dim=2|id=273|"1×1↔1×2")
NDTensors.Dense{Float64, Vector{Float64}}
 2×2
  1.291668655610816    0.5147066921592675
 -0.9207966219752203  -0.687320048765168
 ITensor ord=2
Dim 1: (dim=2|id=727|"1×1↔2×1")
Dim 2: (dim=2|id=845|"2×1↔2×2")
NDTensors.Dense{Float64, Vector{Float64}}
 2×2
 -1.482610874323555   0.2997297254174642
  1.2362723189696732  1.8881470232421553
 ITensor ord=2
Dim 1: (dim=2|id=273|"1×1↔1×2")
Dim 2: (dim=2|id=145|"1×2↔2×2")
NDTensors.Dense{Float64, Vector{Float64}}
 2×2
 -0.3620519644906389  -0.8149107592460717
 -0.6007286129069581  -1.794450655933629
 ITensor ord=2
Dim 1: (dim=2|id=845|"2×1↔2×2")
Dim 2: (dim=2|id=145|"1×2↔2×2")
NDTensors.Dense{Float64, Vector{Float64}}
 2×2
  0.7163493122261674   0.880775856816735
 -0.7909478596586718  -0.5341080305805886

I would have expected it would output an ITensor.

Start by reproducing the shown ITensor(::ITensorNetwork) call in the Julia REPL and locate the constructor entry point. Determine whether the documented or intended result is one ITensor or a collection for a network with four vertices, then confirm the chosen behavior with an appropriate regression test or API clarification.