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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

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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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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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import tensorflow as tf
from tensorflow.python.framework import ops
import numpy as np

# Define custom py_func which takes also a grad op as argument:
def py_func(func, inp, Tout, stateful=True, name=None, grad=None):
    
    # Need to generate a unique name to avoid duplicates:
    rnd_name = 'PyFuncGrad' + str(np.random.randint(0, 1E+8))
    

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class GCSDataset(Dataset):
    """Generic PyTorch dataset for GCS. Streams data from GCS and (optionally) caches to local disk.

    """

    def __init__(self,
                 bucketname=None,
                 path_list=None,  # TODO: list bucket/path contents if None
                 target_list=None,
                 transform=None,

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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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"""
MIT License

knn, kl_div, entropy Copyright (c) 2017 Heikki Arponen
"""

import torch


def knn(x, y, k=3, last_only=False, discard_nearest=True):

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from itertools import islice
from munch import Munch
import sys, os
from torch.utils.data import DataLoader
from torchvision import transforms
import time

import webdataset as wds

sys.path.append(os.getcwd())

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from itertools import islice
import os
import torch
from torch.utils.data import DataLoader
from torchvision import transforms
import numpy as np

import torch_xla.distributed.parallel_loader as pl
import torch_xla.core.xla_model as xm
import torch_xla.distributed.xla_multiprocessing as xmp

harpone

Long-lasting COVID-19 -
Consensus statement of the expert group appointed by STM on 31 December 2021
VN / 20672/2021



DRAFT 7.1.2022