input: Write a function to create a neural network class in Python that models a linear regression function. The class should have an initialization method to initialize the weights and bias, and a method to perform the forward pass. The forward pass should calculate the output of the linear regression model by multiplying the input data with the weights and adding the bias.
  
  
  import numpy as np
  class LinearRegressionModel:
      def __init__(self, input_dim, output_dim):
          ''' Initialises the weights and bias of the linear regression model
          Arguments:
              input_dim {int} -- Number of input features
              output_dim {int} -- Number of outputs
          '''
          self.weights = np.zeros((input_dim, output_dim))
          self.bias = np.zeros((1, output_dim))
  
      def forward(self, x):
          ''' Calculates the forward pass of the linear regression model
          Arguments:
              x {numpy array} -- Input data of size (N, input_dim)
          Returns:
              out {numpy array} -- Output of the linear regression model of size (N, output_dim)
          '''
          out = np.dot(x, self.weights) + self.bias
          return out
  
  
  assert np.array_equal(LinearRegressionModel(3, 1).forward(np.array([[1, 2, 3], [4, 5, 6]])), np.array([[0], [0]]))
  assert np.array_equal(LinearRegressionModel(2, 2).forward(np.array([[1, 2], [3, 4]])), np.array([[0, 0], [0, 0]]))
  
  
output: 
  import numpy as np
  class LinearRegressionModel:
      def __init__(self, input_dim, output_dim):
          self.weights = np.zeros((input_dim, output_dim))
          self.bias = np.zeros((1, output_dim))
  
      def forward(self, x):
          out = np.dot(x, self.weights) + self.bias
          return out
  