nilsleh

import torch
import timm

from torchgeo.datasets import EuroSAT

import numpy as np
from sklearn.linear_model import LogisticRegression
from torch.utils.data import DataLoader
import torch
from tqdm import tqdm

nilsleh

import numpy as np
import pandas as pd

from darts import TimeSeries
from darts.dataprocessing.transformers import Scaler
from darts.models import TFTModel
from darts.datasets import AirPassengersDataset
from darts.utils.timeseries_generation import datetime_attribute_timeseries
from darts.utils.likelihood_models import QuantileRegression
import pytorch_lightning as pl

nilsleh

import numpy as np
import pandas as pd
from tqdm import tqdm_notebook as tqdm

import matplotlib.pyplot as plt

from darts import TimeSeries, concatenate
from darts.dataprocessing.transformers import Scaler
from darts.models import TFTModel
from darts.metrics import mape

nilsleh

import numpy as np
import torch
import torch.nn as nn
from functorch import vmap, jacrev, make_functional_with_buffers

batch_size = 2
in_channels = 5
out_channels = 20
feature_shape = 8
feature = torch.rand(batch_size, in_channels, feature_shape, feature_shape)

nilsleh

import json
import os

import matplotlib.pyplot as plt
import numpy as np
import planetary_computer as pc
import rasterio
import stackstac
from pystac_client import Client
from shapely.geometry import mapping, shape

nilsleh

import matplotlib.pyplot as plt
import numpy as np
import planetary_computer as pc
import stackstac
from pystac_client import Client

import shapely.geometry
import fiona.transform

polygon = {