/-- If a differentiable function `f` is approximated by a linear map `A` on a set `s`, up to `δ`,
then at almost every `x` in `s` one has `‖f' x - A‖ ≤ δ`. -/
theorem _root_.ApproximatesLinearOn.norm_fderiv_sub_le {A : E →L[ℝ] E} {δ : ℝ≥0}
    (hf : ApproximatesLinearOn f A s δ) (hs : MeasurableSet s) (f' : E → E →L[ℝ] E)
    (hf' : ∀ x ∈ s, HasFDerivWithinAt f (f' x) s x) : ∀ᵐ x ∂μ.restrict s, ‖f' x - A‖₊ ≤ δ := by
  /- The conclusion will hold at the Lebesgue density points of `s` (which have full measure).
    At such a point `x`, for any `z` and any `ε > 0` one has for small `r`
    that `{x} + r • closedBall z ε` intersects `s`. At a point `y` in the intersection,
    `f y - f x` is close both to `f' x (r z)` (by differentiability) and to `A (r z)`
    (by linear approximation), so these two quantities are close, i.e., `(f' x - A) z` is small. -/
  filter_upwards [Besicovitch.ae_tendsto_measure_inter_div μ s, ae_restrict_mem hs]
  -- start from a Lebesgue density point `x`, belonging to `s`.
  intro x hx xs
  -- consider an arbitrary vector `z`.
  apply ContinuousLinearMap.op_norm_le_bound _ δ.2 fun z => ?_
  -- to show that `‖(f' x - A) z‖ ≤ δ ‖z‖`, it suffices to do it up to some error that vanishes
  -- asymptotically in terms of `ε > 0`.
  suffices H : ∀ ε, 0 < ε → ‖(f' x - A) z‖ ≤ (δ + ε) * (‖z‖ + ε) + ‖f' x - A‖ * ε
  · have :
      Tendsto (fun ε : ℝ => ((δ : ℝ) + ε) * (‖z‖ + ε) + ‖f' x - A‖ * ε) (𝓝[>] 0)
        (𝓝 ((δ + 0) * (‖z‖ + 0) + ‖f' x - A‖ * 0)) :=
      Tendsto.mono_left (Continuous.tendsto (by continuity) 0) nhdsWithin_le_nhds
    simp only [add_zero, mul_zero] at this
    apply le_of_tendsto_of_tendsto tendsto_const_nhds this
    filter_upwards [self_mem_nhdsWithin]
    exact H
  -- fix a positive `ε`.
  intro ε εpos
  -- for small enough `r`, the rescaled ball `r • closedBall z ε` intersects `s`, as `x` is a
  -- density point
  have B₁ : ∀ᶠ r in 𝓝[>] (0 : ℝ), (s ∩ ({x} + r • closedBall z ε)).Nonempty :=
    eventually_nonempty_inter_smul_of_density_one μ s x hx _ measurableSet_closedBall
      (measure_closedBall_pos μ z εpos).ne'
  obtain ⟨ρ, ρpos, hρ⟩ :
    ∃ ρ > 0, ball x ρ ∩ s ⊆ {y : E | ‖f y - f x - (f' x) (y - x)‖ ≤ ε * ‖y - x‖} :=
    mem_nhdsWithin_iff.1 ((hf' x xs).isLittleO.def εpos)
  -- for small enough `r`, the rescaled ball `r • closedBall z ε` is included in the set where
  -- `f y - f x` is well approximated by `f' x (y - x)`.
  have B₂ : ∀ᶠ r in 𝓝[>] (0 : ℝ), {x} + r • closedBall z ε ⊆ ball x ρ := by
    

Proof state:
E : Type u_1
F : Type u_2
inst✝⁷ : NormedAddCommGroup E
inst✝⁶ : NormedSpace ℝ E
inst✝⁵ : FiniteDimensional ℝ E
inst✝⁴ : NormedAddCommGroup F
inst✝³ : NormedSpace ℝ F
s : Set E
f : E → E
f'✝ : E → E →L[ℝ] E
inst✝² : MeasurableSpace E
inst✝¹ : BorelSpace E
μ : Measure E
inst✝ : IsAddHaarMeasure μ
A : E →L[ℝ] E
δ : ℝ≥0
hf : ApproximatesLinearOn f A s δ
hs : MeasurableSet s
f' : E → E →L[ℝ] E
hf' : ∀ x ∈ s, HasFDerivWithinAt f (f' x) s x
x : E
hx : Tendsto (fun r => ↑↑μ (s ∩ closedBall x r) / ↑↑μ (closedBall x r)) (𝓝[>] 0) (𝓝 1)
xs : x ∈ s
z : E
ε : ℝ
εpos : 0 < ε
B₁ : ∀ᶠ (r : ℝ) in 𝓝[>] 0, Set.Nonempty (s ∩ ({x} + r • closedBall z ε))
ρ : ℝ
ρpos : ρ > 0
hρ : ball x ρ ∩ s ⊆ {y | ‖f y - f x - (f' x) (y - x)‖ ≤ ε * ‖y - x‖}
⊢ ∀ᶠ (r : ℝ) in 𝓝[>] 0, {x} + r • closedBall z ε ⊆ ball x ρ

Next tactic:
apply nhdsWithin_le_nhds