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class Lamb(Optimizer):
    r"""Implements Lamb algorithm.
    It has been proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes`_.
    Arguments:
        params (iterable): iterable of parameters to optimize or dicts defining
            parameter groups
        lr (float, optional): learning rate (default: 1e-3)
        betas (Tuple[float, float], optional): coefficients used for computing
            running averages of gradient and its square (default: (0.9, 0.999))
        eps (float, optional): term added to the denominator to improve

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import numpy as np
cimport cython
cimport numpy as np
 
from libc.stdint cimport uint32_t, int32_t
from libc.math cimport sqrt
from libc.math cimport fabs
from libc.math cimport pow
 
 

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def simulation(L = 0, N = 100000, dt = 1E-3, init = .1):
    """Simulate a stochastic differential equation.
    """
 
    #Set up some parameters:
    f1 = .1
    g1 = .01
    g2 = .1
    
    dW = np.random.randn(N)*np.sqrt(dt)

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# Same with Cython:
import numpy as np
cimport cython
cimport numpy as np

from libc.stdint cimport uint32_t, int32_t
from libc.math cimport sqrt
from libc.math cimport fabs
from libc.math cimport pow