input: Write a function in Python to create a class for a neural network that models a linear regression function. The class should initialize the weights and bias for the model and perform a forward pass calculation. The function should return the output of the linear regression model.
  
  
  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
  
  # Example usage:
  model = LinearRegressionModel(input_dim=3, output_dim=2)
  input_data = np.array([[1, 2, 3], [4, 5, 6]])
  output = model.forward(input_data)
  print(output)
  
  
  assert np.array_equal(LinearRegressionModel(3, 2).forward(np.array([[1, 2, 3], [4, 5, 6]])), np.array([[0, 0], [0, 0]]))==True
  assert np.array_equal(LinearRegressionModel(2, 1).forward(np.array([[1, 2], [3, 4]])), np.array([[0], [0]]))==True
  assert np.array_equal(LinearRegressionModel(1, 1).forward(np.array([[1]])), np.array([[0]]))==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
  