class_docstr: Enables dimensionality reduction for multiple
  class distributions. It transforms the features space into a space where
  the between class scatter is maximized and the within class scatter is
  minimized.
  
  Parameters:
  -----------
  solver: str
      If 'svd' we use the pseudo-inverse to calculate the inverse of matrices
      when doing the transformation.
code: def __init__(self, solver="svd"):
      self.solver = solver
  
file_path: ML-From-Scratch/mlfromscratch/supervised_learning/multi_class_lda.py
imports: from __future__ import print_function, division
  import matplotlib.pyplot
  import numpy
  from mlfromscratch.utils import calculate_covariance_matrix, normalize, standardize
label_desc: Use image node_id 1 to create a new MultiClassLDA object  with example: obj = MultiClassLDA(solver)