input: Write a function to create a neural network class in Python to model a linear regression function. The class should include methods for initializing the model with weights and bias, and for performing a forward pass to calculate the output.
  
  
  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 isinstance(LinearRegressionModel(3, 1), LinearRegressionModel)==True
  assert np.array_equal(LinearRegressionModel(3, 1).forward(np.array([[1, 2, 3]])), np.zeros((1, 1)))==True
  assert np.array_equal(LinearRegressionModel(2, 2).forward(np.array([[1, 2], [3, 4]])), np.zeros((2, 2)))==True
  assert np.array_equal(LinearRegressionModel(1, 3).forward(np.array([[1], [2], [3]])), np.zeros((3, 3)))==True
  assert np.array_equal(LinearRegressionModel(0, 0).forward(np.array([])), np.zeros((0, 0)))==True
output: 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