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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Biomass, synthetic fuels and carbon management
In this example we show how to manage different biomass stocks with different potentials and costs, synthetic fuel production, direct air capture (DAC) and carbon capture and usage/sequestration/cycling (CCU/S/C).
Demand for electricity and diesel transport have to be met from various biomass sources, natural gas with possibility for carbon capture, electrolysis for hydrogen production, direct air capture of CO2, and diesel synthesis via Fischer-Tropsch.
The system has to reach a target of net negative emissions over the period.
All numbers/costs/efficiencies are fictitious to allow easy analysis.
Step1:  First tell PyPSA that links can have multiple outputs by overriding the component_attrs. This can be done for as many buses as you need with format busi for i = 2,3,4,5,.... 
See https
Step2:  Add a constant electrical load
Step3:  Add a constant demand for transport
Step4:  Add a bus for Hydrogen storage.
Step5:  Allow production of diesel from H2 and CO2 using Fischer-Tropsch
Step6:  Direct air capture consumes electricity to take CO2 from the air to the underground store
Step7:  Meet transport with diesel
Step8:  Add a cheap and a expensive biomass generator.
Step9:  Add a global CO$_2$ constraint.
Step10:  How do the different stores in the system behave?
Step11:  Let's have a look at the production
Step12:  At all times, the amount of carbon is constant!
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Python Code:
import pypsa
import numpy as np
import matplotlib.pyplot as plt
Explanation: Biomass, synthetic fuels and carbon management
In this example we show how to manage different biomass stocks with different potentials and costs, synthetic fuel production, direct air capture (DAC) and carbon capture and usage/sequestration/cycling (CCU/S/C).
Demand for electricity and diesel transport have to be met from various biomass sources, natural gas with possibility for carbon capture, electrolysis for hydrogen production, direct air capture of CO2, and diesel synthesis via Fischer-Tropsch.
The system has to reach a target of net negative emissions over the period.
All numbers/costs/efficiencies are fictitious to allow easy analysis.
End of explanation
override_component_attrs = pypsa.descriptors.Dict(
    {k: v.copy() for k, v in pypsa.components.component_attrs.items()}
)
override_component_attrs["Link"].loc["bus2"] = [
    "string",
    np.nan,
    np.nan,
    "2nd bus",
    "Input (optional)",
]
override_component_attrs["Link"].loc["bus3"] = [
    "string",
    np.nan,
    np.nan,
    "3rd bus",
    "Input (optional)",
]
override_component_attrs["Link"].loc["efficiency2"] = [
    "static or series",
    "per unit",
    1.0,
    "2nd bus efficiency",
    "Input (optional)",
]
override_component_attrs["Link"].loc["efficiency3"] = [
    "static or series",
    "per unit",
    1.0,
    "3rd bus efficiency",
    "Input (optional)",
]
override_component_attrs["Link"].loc["p2"] = [
    "series",
    "MW",
    0.0,
    "2nd bus output",
    "Output",
]
override_component_attrs["Link"].loc["p3"] = [
    "series",
    "MW",
    0.0,
    "3rd bus output",
    "Output",
]
n = pypsa.Network(override_component_attrs=override_component_attrs)
n.set_snapshots(range(10))
Explanation: First tell PyPSA that links can have multiple outputs by overriding the component_attrs. This can be done for as many buses as you need with format busi for i = 2,3,4,5,.... 
See https://pypsa.org/doc/components.html#link-with-multiple-outputs-or-inputs
End of explanation
n.add("Bus", "bus")
n.add("Load", "load", bus="bus", p_set=1.0)
Explanation: Add a constant electrical load
End of explanation
n.add("Bus", "transport")
n.add("Load", "transport", bus="transport", p_set=1.0)
n.add("Bus", "diesel")
n.add("Store", "diesel", bus="diesel", e_cyclic=True, e_nom=1000.0)
Explanation: Add a constant demand for transport
End of explanation
n.add("Bus", "hydrogen")
n.add("Store", "hydrogen", bus="hydrogen", e_cyclic=True, e_nom=1000.0)
# n.add("Load","hydrogen",
#      bus="hydrogen",
#      p_set=1.)
n.add("Link", "electrolysis", p_nom=2.0, efficiency=0.8, bus0="bus", bus1="hydrogen")
Explanation: Add a bus for Hydrogen storage.
End of explanation
n.add(
    "Link",
    "FT",
    p_nom=4,
    bus0="hydrogen",
    bus1="diesel",
    bus2="co2 stored",
    efficiency=1.0,
    efficiency2=-1,
)
# minus sign because opposite to how fossil fuels used:
# CH4 burning puts CH4 down, atmosphere up
n.add("Carrier", "co2", co2_emissions=-1.0)
# this tracks CO2 in the atmosphere
n.add("Bus", "co2 atmosphere", carrier="co2")
# NB: can also be negative
n.add("Store", "co2 atmosphere", e_nom=1000, e_min_pu=-1, bus="co2 atmosphere")
# this tracks CO2 stored, e.g. underground
n.add("Bus", "co2 stored")
# NB: can also be negative
n.add("Store", "co2 stored", e_nom=1000, e_min_pu=-1, bus="co2 stored")
Explanation: Allow production of diesel from H2 and CO2 using Fischer-Tropsch
End of explanation
n.add(
    "Link",
    "DAC",
    bus0="bus",
    bus1="co2 stored",
    bus2="co2 atmosphere",
    efficiency=1,
    efficiency2=-1,
    p_nom=5.0,
)
Explanation: Direct air capture consumes electricity to take CO2 from the air to the underground store
End of explanation
n.add(
    "Link",
    "diesel car",
    bus0="diesel",
    bus1="transport",
    bus2="co2 atmosphere",
    efficiency=1.0,
    efficiency2=1.0,
    p_nom=2.0,
)
n.add("Bus", "gas")
n.add("Store", "gas", e_initial=50, e_nom=50, marginal_cost=20, bus="gas")
n.add(
    "Link",
    "OCGT",
    bus0="gas",
    bus1="bus",
    bus2="co2 atmosphere",
    p_nom_extendable=True,
    efficiency=0.5,
    efficiency2=1,
)
n.add(
    "Link",
    "OCGT+CCS",
    bus0="gas",
    bus1="bus",
    bus2="co2 stored",
    bus3="co2 atmosphere",
    p_nom_extendable=True,
    efficiency=0.4,
    efficiency2=0.9,
    efficiency3=0.1,
)
Explanation: Meet transport with diesel
End of explanation
biomass_marginal_cost = [20.0, 50.0]
biomass_stored = [40.0, 15.0]
for i in range(2):
    n.add("Bus", "biomass" + str(i))
    n.add(
        "Store",
        "biomass" + str(i),
        bus="biomass" + str(i),
        e_nom_extendable=True,
        marginal_cost=biomass_marginal_cost[i],
        e_nom=biomass_stored[i],
        e_initial=biomass_stored[i],
    )
    # simultaneously empties and refills co2 atmosphere
    n.add(
        "Link",
        "biomass" + str(i),
        bus0="biomass" + str(i),
        bus1="bus",
        p_nom_extendable=True,
        efficiency=0.5,
    )
    n.add(
        "Link",
        "biomass+CCS" + str(i),
        bus0="biomass" + str(i),
        bus1="bus",
        bus2="co2 stored",
        bus3="co2 atmosphere",
        p_nom_extendable=True,
        efficiency=0.4,
        efficiency2=1.0,
        efficiency3=-1,
    )
# can go to -50, but at some point can't generate enough electricity for DAC and demand
target = -50
Explanation: Add a cheap and a expensive biomass generator.
End of explanation
n.add(
    "GlobalConstraint",
    "co2_limit",
    sense="<=",
    carrier_attribute="co2_emissions",
    constant=target,
)
n.lopf();
Explanation: Add a global CO$_2$ constraint.
End of explanation
n.stores_t.e.plot(figsize=(9, 7), lw=3)
plt.tight_layout()
Explanation: How do the different stores in the system behave?
End of explanation
n.links_t.p0[["biomass+CCS0", "biomass+CCS1", "OCGT+CCS", "DAC"]].plot(
    subplots=True, figsize=(9, 7)
)
plt.tight_layout()
Explanation: Let's have a look at the production
End of explanation
n.stores_t.e[["co2 stored", "co2 atmosphere", "gas", "diesel"]].sum(axis=1)
Explanation: At all times, the amount of carbon is constant!
End of explanation
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