input: Write a function to create a neural network class in Python to model a linear regression function. The class should include an initializer to set up the weights and bias, and a forward method to compute the forward pass. The weights and bias should be initialized to zero.
  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 (LinearRegressionModel(3, 1).weights == np.zeros((3, 1))).all() == True
  assert (LinearRegressionModel(3, 1).bias == np.zeros((1, 1))).all() == True
  x = np.array([[1, 2, 3], [4, 5, 6]])
  model = LinearRegressionModel(3, 1)
  assert (model.forward(x) == np.zeros((2, 1))).all() == True
  assert (model.forward(x) == np.dot(x, model.weights) + model.bias).all() == True
output: 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