class_docstr: Suggests an architecture based on the Harmonica algorithm.
docstr: Calculates fourier basis.
  
  Args:
    t: integer.
    k: integer.
  
  Returns:
    Returns the following value:
    \Phi_t(k) = e^{(2\pi i k)/len(self._block_indices)}
docstr_tok: ["Calculates","fourier","basis",".","Args",":","t",":","integer",".","k",":","integer",".","Returns",":","Returns","the","following","value",":","\\Phi_t","(","k",")","=","e^","{","(","2\\pi","i","k",")","\/len","(","self._block_indices",")","}"]
code: def basis_function(self, t, k):
      r"""Calculates fourier basis.
  
      Args:
        t: integer.
        k: integer.
  
      Returns:
        Returns the following value:
        \Phi_t(k) = e^{(2\pi i k)/len(self._block_indices)}
      """
      value = cmath.exp(
          2
          * cmath.pi
          * complex(0, 1)
          * t
          * k
          / len(self._block_indices)
      )
      return value
  
imports: import cmath
  import os
  from absl import logging
  from model_search import block_builder
  from model_search.architecture import architecture_utils
  from model_search.search import search_algorithm
  import numpy
  from sklearn import linear_model
  from sklearn import preprocessing
  import tensorflow.compat.v2
label_desc: Use image node_id 3 for calling the Harmonica obj's underlying member method code with example usage: obj.basis_function(t, k) and returns: value