ENH: ensure code that is 80 characters wide is visible for code-cell

We have adopted an 80 character wide line limit for code in code-cells. 

We should update the theme to ensure the 80 character width is always visible so there is no scrolling required right <-> left

Here is some example code

````md
```{code-cell} python
class SamuelsonLSS(LinearStateSpace):
 """
 This subclass creates a Samuelson multiplier-accelerator model
 as a linear state space system.
 """

 def __init__(self, y_0=100, y_1=50, α=0.8, β=0.9, γ=10, σ=1, g=10):

 self.α, self.β = α, β
 self.y_0, self.y_1, self.g = y_0, y_1, g
 self.γ, self.σ = γ, σ

 # Define intial conditions
 self.μ_0 = [1, y_0, y_1]

 self.ρ1 = α + β
 self.ρ2 = -β

 # Define transition matrix
 self.A = [[1, 0, 0], [γ + g, self.ρ1, self.ρ2], [0, 1, 0]]

 # Define output matrix
 self.G = [
 [γ + g, self.ρ1, self.ρ2], # this is Y_{t+1}
 [γ, α, 0], # this is C_{t+1}
 [0, β, -β],
 ] # this is I_{t+1}

 self.C = np.zeros((3, 1))
 self.C[1] = σ # stochastic

 # Initialize LSS with parameters from Samuelson model
 LinearStateSpace.__init__(self, self.A, self.C, self.G, mu_0=self.μ_0)

 def plot_simulation(self, ts_length=100, stationary=True):

 # Temporarily store original parameters
 temp_mu = self.mu_0
 temp_Sigma = self.Sigma_0

 # Set distribution parameters equal to their stationary
 # values for simulation
 if stationary == True:
 try:
 self.mu_x, self.mu_y, self.Sigma_x, self.Sigma_y, self.Sigma_yx = (
 self.stationary_distributions()
 )
 self.mu_0 = self.mu_x
 self.Sigma_0 = self.Sigma_x
 # Exception where no convergence achieved when
 # calculating stationary distributions
 except ValueError:
 print("Stationary distribution does not exist")

 x, y = self.simulate(ts_length)

 fig, axes = plt.subplots(3, 1, sharex=True, figsize=(12, 8))
 titles = ["Output ($Y_t$)", "Consumption ($C_t$)", "Investment ($I_t$)"]
 colors = ["darkblue", "red", "purple"]
 for ax, series, title, color in zip(axes, y, titles, colors):
 ax.plot(series, color=color)
 ax.set(title=title, xlim=(0, n))
 ax.grid()

 axes[-1].set_xlabel("Iteration")

 # Reset distribution parameters to their initial values
 self.mu_0 = temp_mu
 self.Sigma_0 = temp_Sigma

 return fig

 def plot_irf(self, j=5):

 x, y = self.impulse_response(j)

 # Reshape into 3 x j matrix for plotting purposes
 yimf = np.array(y).flatten().reshape(j + 1, 3).T

 fig, axes = plt.subplots(3, 1, sharex=True, figsize=(12, 8))
 labels = ["$Y_t$", "$C_t$", "$I_t$"]
 colors = ["darkblue", "red", "purple"]
 for ax, series, label, color in zip(axes, yimf, labels, colors):
 ax.plot(series, color=color)
 ax.set(xlim=(0, j))
 ax.set_ylabel(label, rotation=0, fontsize=14, labelpad=10)
 ax.grid()

 axes[0].set_title("Impulse Response Functions")
 axes[-1].set_xlabel("Iteration")

 return fig

 def multipliers(self, j=5):
 x, y = self.impulse_response(j)
 return np.sum(np.array(y).flatten().reshape(j + 1, 3), axis=0)
```
````

that `Jupyter-book` converts to 


```
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre id="codecell42">class SamuelsonLSS(LinearStateSpace):
 """
 This subclass creates a Samuelson multiplier-accelerator model
 as a linear state space system.
 """

 def __init__(self, y_0=100, y_1=50, α=0.8, β=0.9, γ=10, σ=1, g=10):

 self.α, self.β = α, β
 self.y_0, self.y_1, self.g = y_0, y_1, g
 self.γ, self.σ = γ, σ

 # Define intial conditions
 self.μ_0 = [1, y_0, y_1]

 self.ρ1 = α + β
 self.ρ2 = -β

 # Define transition matrix
 self.A = [[1, 0, 0], [γ + g, self.ρ1, self.ρ2], [0, 1, 0]]

 # Define output matrix
 self.G = [
 [γ + g, self.ρ1, self.ρ2], # this is Y_{t+1}
 [γ, α, 0], # this is C_{t+1}
 [0, β, -β],
 ] # this is I_{t+1}

 self.C = np.zeros((3, 1))
 self.C[1] = σ # stochastic

 # Initialize LSS with parameters from Samuelson model
 LinearStateSpace.__init__(self, self.A, self.C, self.G, mu_0=self.μ_0)

 def plot_simulation(self, ts_length=100, stationary=True):

 # Temporarily store original parameters
 temp_mu = self.mu_0
 temp_Sigma = self.Sigma_0

 # Set distribution parameters equal to their stationary
 # values for simulation
 if stationary == True:
 try:
 self.mu_x, self.mu_y, self.Sigma_x, self.Sigma_y, self.Sigma_yx = (
 self.stationary_distributions()
 )
 self.mu_0 = self.mu_x
 self.Sigma_0 = self.Sigma_x
 # Exception where no convergence achieved when
 # calculating stationary distributions
 except ValueError:
 print("Stationary distribution does not exist")

 x, y = self.simulate(ts_length)

 fig, axes = plt.subplots(3, 1, sharex=True, figsize=(12, 8))
 titles = ["Output ($Y_t$)", "Consumption ($C_t$)", "Investment ($I_t$)"]
 colors = ["darkblue", "red", "purple"]
 for ax, series, title, color in zip(axes, y, titles, colors):
 ax.plot(series, color=color)
 ax.set(title=title, xlim=(0, n))
 ax.grid()

 axes[-1].set_xlabel("Iteration")

 # Reset distribution parameters to their initial values
 self.mu_0 = temp_mu
 self.Sigma_0 = temp_Sigma

 return fig

 def plot_irf(self, j=5):

 x, y = self.impulse_response(j)

 # Reshape into 3 x j matrix for plotting purposes
 yimf = np.array(y).flatten().reshape(j + 1, 3).T

 fig, axes = plt.subplots(3, 1, sharex=True, figsize=(12, 8))
 labels = ["$Y_t$", "$C_t$", "$I_t$"]
 colors = ["darkblue", "red", "purple"]
 for ax, series, label, color in zip(axes, yimf, labels, colors):
 ax.plot(series, color=color)
 ax.set(xlim=(0, j))
 ax.set_ylabel(label, rotation=0, fontsize=14, labelpad=10)
 ax.grid()

 axes[0].set_title("Impulse Response Functions")
 axes[-1].set_xlabel("Iteration")

 return fig

 def multipliers(self, j=5):
 x, y = self.impulse_response(j)
 return np.sum(np.array(y).flatten().reshape(j + 1, 3), axis=0)
</pre><button class="copybtn o-tooltip--left" data-tooltip="Copy" data-clipboard-target="#codecell42"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-copy" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
 <title>Copy to clipboard</title>
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```

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ENH: ensure code that is 80 characters wide is visible for code-cell #308

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