code: def pdf_between_data(train_data, X, std):
      """
      Compute PDF between two samples.
  
      Parameters
      ----------
      train_data : array
          Training dataset.
  
      X : array
          Input dataset
  
      std : float
          Standard deviation for Probability Density
          Function (PDF).
  
      Returns
      -------
      array-like
      """
      n_train_samples = train_data.shape[0]
      n_samples = X.shape[0]
  
      results = np.zeros((n_train_samples, n_samples))
      variance = std**2
      const = std * math.sqrt(2 * math.pi)
  
      for i, input_row in enumerate(X):
          inputs = np.tile(input_row, (n_train_samples, 1))
          class_difference = train_data - inputs
          total_distance = inner1d(class_difference, class_difference)
          results[:, i] = np.exp(-total_distance / variance) / const
  
      return results
  
imports: import math
  import numpy
  from numpy.core.umath_tests import inner1d
label_desc: Use image node_id 1 for calling a global function with example usage: pdf_between_data(train_data, X, std) and returns: results