class_docstr: Student Skew t Distribution
  ----
  This class contains methods relating to the Student Skew t distribution for time series.
code: def logpdf_internal(x, df, loc=0.0, scale=1.0, gamma=1.0):
      result = np.zeros(x.shape[0])
      result[x - loc < 0] = (
          np.log(2.0)
          - np.log(gamma + 1.0 / gamma)
          + ss.t.logpdf(
              x=gamma * x[(x - loc) < 0],
              loc=loc[(x - loc) < 0] * gamma,
              df=df,
              scale=scale,
          )
      )
      result[x - loc >= 0] = (
          np.log(2.0)
          - np.log(gamma + 1.0 / gamma)
          + ss.t.logpdf(
              x=x[(x - loc) >= 0] / gamma,
              loc=loc[(x - loc) >= 0] / gamma,
              df=df,
              scale=scale,
          )
      )
      return result
  
imports: import numpy
  import scipy.stats
  import scipy.special
  from .family import Family
  from .flat import Flat
  from .normal import Normal
  from .gas_recursions import gas_recursion_skewt_orderone, gas_recursion_skewt_ordertwo
  from .gas_recursions import gasx_recursion_skewt_orderone, gasx_recursion_skewt_ordertwo
  from .gas_recursions import gas_llev_recursion_skewt_orderone, gas_llev_recursion_skewt_ordertwo
  from .gas_recursions import gas_llt_recursion_skewt_orderone, gas_llt_recursion_skewt_ordertwo
  from .gas_recursions import gas_reg_recursion_skewt_orderone, gas_reg_recursion_skewt_ordertwo
label_desc: Use image node_id 8 for calling the Skewt obj's underlying member method code with example usage: obj.logpdf_internal(x, df, loc, scale, gamma) and returns: result