Decision Optimization

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n_xvars=2

n_yvars=2

#Create model:
my_MILP=Model(name='MILP example')

# Defining variables:
x_vars={i+1:my_MILP.continuous_var(name='x_{}'.format(i+1),lb=-20,ub=20) for i in range(n_xvars)}

y_vars={i+1:my_MILP.integer_var(name='y_{}'.format(i+1),lb=-20,ub=20) for i in range(n_yvars)}

# Adding constraints:

my_MILP.add_constraint(x_vars[1]+4*x_vars[2]>=-5)
my_MILP.add_constraint(x_vars[1]-y_vars[2]<=5)
my_MILP.add_constraint(x_vars[1]+3*y_vars[1]-10*y_vars[2]<=10)
my_MILP.add_constraint(x_vars[1]+2*x_vars[2]-3*y_vars[1]+4*y_vars[2]<=2)

# Defining objective function:
obj=x_vars[1]+5*x_vars[2]+6*y_vars[1]-2*y_vars[2]
my_MILP.minimize(obj)

# Model information
my_MILP.print_information()
print(my_MILP.export_as_lp_string())

# Solving and solution
my_MILP.solve()
my_MILP.print_solution()

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I have used a few codes such as 

nd_count=my_MILP.get_solve_details().nodes_processed

nd_left=my_MILP.get_solve_details().nodes_left

but I am getting errors like

AttributeError: 'SolveDetails' object has no attribute 'nodes_left'

AttributeError: 'SolveDetails' object has no attribute 'nodes_processed'

I request the community to please help me obtain the node count and nodes left for MILP, IP, etc.