Add _is_real for GenericArrayExpr and restore full tests

JuMP's GenericNonlinearExpr constructor validates arguments via
_is_real(). GenericArrayExpr was missing this method, causing norm()
to fail when wrapping array expressions in NonlinearExpr. Also
consolidate the L2 loss tests into a single comprehensive test.

https://claude.ai/code/session_01GWT1QHA3D5BpMQBEHvgbcV

Author: claude

src/JuMP/nlp_expr.jl

@@ -20,3 +20,5 @@ end
 Base.size(expr::GenericArrayExpr) = expr.size
 
 JuMP.variable_ref_type(::Type{GenericArrayExpr{V,N}}) where {V,N} = V
+
+JuMP._is_real(::GenericArrayExpr) = true

test/JuMP.jl

@@ -120,7 +120,7 @@ function test_l2_loss_tanh()
     return
 end
 
-function test_l2_loss_nested()
+function test_l2_loss()
     n = 2
     X = rand(n, n)
     Y = rand(n, n)
@@ -130,12 +130,14 @@ function test_l2_loss_nested()
     Y_hat = W2 * tanh.(W1 * X)
     diff_expr = Y_hat .- Y
     @test diff_expr isa ArrayDiff.MatrixExpr
-    # Test creating NonlinearExpr manually
-    loss = JuMP.GenericNonlinearExpr{JuMP.VariableRef}(
-        :norm,
-        Any[diff_expr],
-    )
+    @test diff_expr.head == :-
+    @test diff_expr.broadcasted
+    @test diff_expr.args[1] === Y_hat
+    @test diff_expr.args[2] === Y
+    loss = LinearAlgebra.norm(diff_expr)
     @test loss isa JuMP.NonlinearExpr
+    @test loss.head == :norm
+    @test loss.args[1] === diff_expr
     return
 end