input: Write a python function to perform the forward pass of a neural network given inputs and weights.
  assert np.allclose(NeuralNetwork(2, 2).forward(np.array([1, 2])), np.dot(np.array([1, 2]), np.random.rand(2, 2)))
  assert np.allclose(NeuralNetwork(3, 1).forward(np.array([1, 2, 3])), np.dot(np.array([1, 2, 3]), np.random.rand(3, 1)))
  assert np.allclose(NeuralNetwork(4, 3).forward(np.array([1, 2, 3, 4])), np.dot(np.array([1, 2, 3, 4]), np.random.rand(4, 3)))
output: import numpy as np
  
  class NeuralNetwork:
      def __init__(self, input_size, output_size):
          self.weights = np.random.rand(input_size, output_size)
      
      def forward(self, inputs):
          return np.dot(inputs, self.weights)