from baselines.common.mpi_adam import MpiAdam import numpy as np # Define the parameters for the optimizer params = np.zeros((100,)) optimizer = MpiAdam(params) # Update the parameters for i in range(1000): # Compute gradients gradients = np.random.randn(100) # Update the optimizer optimizer.update(gradients, learning_rate=0.01)In the above example, we initialize the parameters and the optimizer. We generate random gradients and update the optimizer with a learning rate of 0.01. This process repeats for 1000 iterations. The example shows how MpiAdam optimizes gradients for parameters in a distributed manner, which reduces the training time, making it more efficient in training large-scale models. The package library is Baselines.