input: Write a function to create a neural network class in Python to model a linear regression function. The class should have an initialization method to set the weights and bias, and a forward method to compute the output given input data.
  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, 2).forward(np.array([[1, 2, 3], [4, 5, 6]])), np.zeros((2, 2)))
  assert np.array_equal(LinearRegressionModel(2, 1).forward(np.array([[1, 2], [3, 4]])), np.zeros((2, 1)))
  assert np.array_equal(LinearRegressionModel(1, 1).forward(np.array([[1], [2], [3]])), np.zeros((3, 1)))
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