To solve a system of ODEs (ordinary differential equations) in Python, you can use the `scipy.integrate`

module. Here’s an example of how to solve a system of ODEs in Python:

```
import numpy as np
from scipy.integrate import odeint
# Define the system of ODEs
def system(y, t):
x, y = y
dxdt = x * (3 - x - 2*y)
dydt = y * (2 - x - y)
return [dxdt, dydt]
# Define the initial conditions
y0 = [1, 1]
# Define the time points at which to solve the ODEs
t = np.linspace(0, 10, 101)
# Solve the ODEs
sol = odeint(system, y0, t)
# Print the solution
print(sol)
```

In this example, we first import the `numpy`

and `scipy.integrate`

modules using `import numpy as np`

and `from scipy.integrate import odeint`

.

We define the system of ODEs using a function called `system`

. This function takes in two arguments: the state `y`

and the time `t`

. The state `y`

is a list containing the values of `x`

and `y`

, and the function returns a list containing the values of `dx/dt`

and `dy/dt`

.

We define the initial conditions for `x`

and `y`

using `y0 = [1, 1]`

.

We define the time points at which to solve the ODEs using `t = np.linspace(0, 10, 101)`

. This creates an array of 101 equally spaced time points between 0 and 10.

We solve the ODEs using `odeint(system, y0, t)`

. This function takes in the system of ODEs, the initial conditions, and the time points at which to solve the ODEs, and returns a solution as an array.

Finally, we print the solution using `print(sol)`

.

Note: You’ll need to modify the code to define your own system of ODEs and initial conditions.

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