input: Write a function to create a neural network class in Python to model a linear regression function. The class should include an initialization method to set the weights and bias, and a forward method to compute the output of the linear regression model. The function should take input dimensions and output dimensions as parameters.
  
  
  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).weights, np.zeros((3, 1)))==True
  assert np.array_equal(LinearRegressionModel(3, 1).bias, np.zeros((1, 1)))==True
  sample_input = np.array([[1, 2, 3]])
  model = LinearRegressionModel(3, 1)
  assert np.array_equal(model.forward(sample_input), np.zeros((1, 1)))==True
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
  