code: def linear_trend_timewise(x, param=[{"attr": "pvalue"}]):
      ix = x.index
  
      # Get differences between each timestamp and the first timestamp in seconds.
      # Then convert to hours and reshape for linear regression
      times_seconds = (ix - ix[0]).total_seconds()
      times_hours = np.asarray(times_seconds / float(3600))
  
      linReg = linregress(times_hours, x.values)
  
      return [
          (
              'attr_"{}"'.format(config["attr"]),
              getattr(linReg, config["attr"]),
          )
          for config in param
      ]
  
imports: import pandas
  import numpy
  from statsmodels.tsa.stattools import acf, adfuller, pacf
  import itertools
  from statsmodels.tsa.ar_model import AR
  from scipy.signal import cwt, find_peaks_cwt, ricker, welch
  from scipy.stats import linregress
  import scipy.stats
  from scipy.stats import kurtosis
  from scipy.stats import skew
  import numpy
  from scipy import signal, integrate
  from scipy.interpolate import interp1d
  import math
  from statsmodels.tools.sm_exceptions import MissingDataError
  from numpy.linalg import LinAlgError
label_desc: Use image node_id 26 for calling a global function with example usage: linear_trend_timewise(x, param) and returns: unknown