/-- If the target space is complete, the space of continuous multilinear maps with its norm is also
complete. The proof is essentially the same as for the space of continuous linear maps (modulo the
addition of `Finset.prod` where needed. The duplication could be avoided by deducing the linear
case from the multilinear case via a currying isomorphism. However, this would mess up imports,
and it is more satisfactory to have the simplest case as a standalone proof. -/
instance completeSpace [CompleteSpace G] : CompleteSpace (ContinuousMultilinearMap 𝕜 E G) := by
  have nonneg : ∀ v : ∀ i, E i, 0 ≤ ∏ i, ‖v i‖ := fun v =>
    Finset.prod_nonneg fun i _ => norm_nonneg _
  -- We show that every Cauchy sequence converges.
  refine' Metric.complete_of_cauchySeq_tendsto fun f hf => _
  -- We now expand out the definition of a Cauchy sequence,
  rcases cauchySeq_iff_le_tendsto_0.1 hf with ⟨b, b0, b_bound, b_lim⟩
  -- and establish that the evaluation at any point `v : Π i, E i` is Cauchy.
  have cau : ∀ v, CauchySeq fun n => f n v := by
    intro v
    apply cauchySeq_iff_le_tendsto_0.2 ⟨fun n => b n * ∏ i, ‖v i‖, _, _, _⟩
    · intro
      exact mul_nonneg (b0 _) (nonneg v)
    · intro n m N hn hm
      rw [dist_eq_norm]
      apply le_trans ((f n - f m).le_op_norm v) _
      exact mul_le_mul_of_nonneg_right (b_bound n m N hn hm) (nonneg v)
    · simpa using b_lim.mul tendsto_const_nhds
  -- We assemble the limits points of those Cauchy sequences
  -- (which exist as `G` is complete)
  -- into a function which we call `F`.
  choose F hF using fun v => cauchySeq_tendsto_of_complete (cau v)
  -- Next, we show that this `F` is multilinear,
  let Fmult : MultilinearMap 𝕜 E G :=
    { toFun := F
      map_add' := fun v i x y => by
        skip
        have A := hF (Function.update v i (x + y))
        have B := (hF (Function.update v i x)).add (hF (Function.update v i y))
        simp? at A B says simp only [map_add] at A B
        exact tendsto_nhds_unique A B
      map_smul' := fun v i c x => by
        skip
        have A := hF (Function.update v i (c • x))
        have B := Filter.Tendsto.smul (@tendsto_const_nhds _ ℕ _ c _) (hF (Function.update v i x))
        simp? at A B says simp only [map_smul] at A B
        exact tendsto_nhds_unique A B }
  -- and that `F` has norm at most `(b 0 + ‖f 0‖)`.
  have Fnorm : ∀ v, ‖F v‖ ≤ (b 0 + ‖f 0‖) * ∏ i, ‖v i‖ := by
    intro v
    have A : ∀ n, ‖f n v‖ ≤ (b 0 + ‖f 0‖) * ∏ i, ‖v i‖ := by
      intro n
      

Proof state:
𝕜 : Type u
ι : Type v
ι' : Type v'
n✝ : ℕ
E : ι → Type wE
E₁ : ι → Type wE₁
E' : ι' → Type wE'
Ei : Fin (Nat.succ n✝) → Type wEi
G : Type wG
G' : Type wG'
inst✝¹⁵ : Fintype ι
inst✝¹⁴ : Fintype ι'
inst✝¹³ : NontriviallyNormedField 𝕜
inst✝¹² : (i : ι) → NormedAddCommGroup (E i)
inst✝¹¹ : (i : ι) → NormedSpace 𝕜 (E i)
inst✝¹⁰ : (i : ι) → NormedAddCommGroup (E₁ i)
inst✝⁹ : (i : ι) → NormedSpace 𝕜 (E₁ i)
inst✝⁸ : (i : ι') → NormedAddCommGroup (E' i)
inst✝⁷ : (i : ι') → NormedSpace 𝕜 (E' i)
inst✝⁶ : (i : Fin (Nat.succ n✝)) → NormedAddCommGroup (Ei i)
inst✝⁵ : (i : Fin (Nat.succ n✝)) → NormedSpace 𝕜 (Ei i)
inst✝⁴ : NormedAddCommGroup G
inst✝³ : NormedSpace 𝕜 G
inst✝² : NormedAddCommGroup G'
inst✝¹ : NormedSpace 𝕜 G'
c : 𝕜
f✝ g : ContinuousMultilinearMap 𝕜 E G
m : (i : ι) → E i
inst✝ : CompleteSpace G
nonneg : ∀ (v : (i : ι) → E i), 0 ≤ ∏ i : ι, ‖v i‖
f : ℕ → ContinuousMultilinearMap 𝕜 E G
hf : CauchySeq f
b : ℕ → ℝ
b0 : ∀ (n : ℕ), 0 ≤ b n
b_bound : ∀ (n m N : ℕ), N ≤ n → N ≤ m → dist (f n) (f m) ≤ b N
b_lim : Tendsto b atTop (𝓝 0)
cau : ∀ (v : (i : ι) → E i), CauchySeq fun n => (f n) v
F : ((i : ι) → E i) → G
hF : ∀ (v : (i : ι) → E i), Tendsto (fun n => (f n) v) atTop (𝓝 (F v))
Fmult : MultilinearMap 𝕜 E G :=
  { toFun := F,
    map_add' :=
      (_ :
        ∀ [inst : DecidableEq ι] (v : (i : ι) → E i) (i : ι) (x y : E i),
          F (Function.update v i (x + y)) = F (Function.update v i x) + F (Function.update v i y)),
    map_smul' :=
      (_ :
        ∀ [inst : DecidableEq ι] (v : (i : ι) → E i) (i : ι) (c : 𝕜) (x : E i),
          F (Function.update v i (c • x)) = c • F (Function.update v i x)) }
v : (i : ι) → E i
n : ℕ
⊢ ‖(f n) v‖ ≤ (b 0 + ‖f 0‖) * ∏ i : ι, ‖v i‖

Next tactic:
apply le_trans ((f n).le_op_norm _) _