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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:
 AMPLPY
Step1:  Google Colab & Kaggle interagration
Step2:  Use %%ampl_eval to evaluate AMPL commands
Step3:  Use %%writeifile to create files
Step4:  Use %%ampl_eval to run the script cut2.run
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Python Code:
!pip install -q amplpy ampltools
Explanation: AMPLPY: Google Colab & Kaggle Template
Documentation: http://amplpy.readthedocs.io
GitHub Repository: https://github.com/ampl/amplpy
PyPI Repository: https://pypi.python.org/pypi/amplpy
Jupyter Notebooks: https://github.com/ampl/amplpy/tree/master/notebooks
Setup
End of explanation
MODULES=['ampl', 'coin']
from ampltools import cloud_platform_name, ampl_notebook
from amplpy import AMPL, register_magics
if cloud_platform_name() is None:
    ampl = AMPL() # Use local installation of AMPL
else:
    ampl = ampl_notebook(modules=MODULES) # Install AMPL and use it
register_magics(ampl_object=ampl) # Evaluate %%ampl_eval cells with ampl.eval()
Explanation: Google Colab & Kaggle interagration
End of explanation
%%ampl_eval
option version;
Explanation: Use %%ampl_eval to evaluate AMPL commands
End of explanation
%%writefile cut2.mod
  problem Cutting_Opt;
# ----------------------------------------
param nPAT integer >= 0, default 0;
param roll_width;
set PATTERNS = 1..nPAT;
set WIDTHS;
param orders {WIDTHS} > 0;
param nbr {WIDTHS,PATTERNS} integer >= 0;
   check {j in PATTERNS}: sum {i in WIDTHS} i * nbr[i,j] <= roll_width;
var Cut {PATTERNS} integer >= 0;
minimize Number: sum {j in PATTERNS} Cut[j];
subject to Fill {i in WIDTHS}:
   sum {j in PATTERNS} nbr[i,j] * Cut[j] >= orders[i];
  problem Pattern_Gen;
# ----------------------------------------
param price {WIDTHS} default 0;
var Use {WIDTHS} integer >= 0;
minimize Reduced_Cost:
   1 - sum {i in WIDTHS} price[i] * Use[i];
subject to Width_Limit:
   sum {i in WIDTHS} i * Use[i] <= roll_width;
%%writefile cut.dat
data;
param roll_width := 110 ;
param: WIDTHS: orders :=
          20     48
          45     35
          50     24
          55     10
          75      8  ;
%%writefile cut2.run
# ----------------------------------------
# GILMORE-GOMORY METHOD FOR
# CUTTING STOCK PROBLEM
# ----------------------------------------
option solver cbc;
option solution_round 6;
model cut2.mod;
data cut.dat;
problem Cutting_Opt;
   option relax_integrality 1;
   option presolve 0;
problem Pattern_Gen;
   option relax_integrality 0;
   option presolve 1;
let nPAT := 0;
for {i in WIDTHS} {
   let nPAT := nPAT + 1;
   let nbr[i,nPAT] := floor (roll_width/i);
   let {i2 in WIDTHS: i2 <> i} nbr[i2,nPAT] := 0;
   };
repeat {
   solve Cutting_Opt;
   let {i in WIDTHS} price[i] := Fill[i].dual;
   solve Pattern_Gen;
   if Reduced_Cost < -0.00001 then {
      let nPAT := nPAT + 1;
      let {i in WIDTHS} nbr[i,nPAT] := Use[i];
      }
   else break;
   };
display nbr; 
display Cut;
option Cutting_Opt.relax_integrality 0;
option Cutting_Opt.presolve 10;
solve Cutting_Opt;
display Cut;
Explanation: Use %%writeifile to create files
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%%ampl_eval
commands cut2.run;
Explanation: Use %%ampl_eval to run the script cut2.run
End of explanation
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