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 given an input. If the given input is empty, the forward method should return an empty numpy array.
  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)
          '''
          if x.size == 0:
              return np.array([])
          out = np.dot(x, self.weights) + self.bias
          return out
  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], [2], [3]])), np.array([[0], [0], [0]]))==True
  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(0, 0).forward(np.array([])), np.array([]))==True
  assert np.array_equal(LinearRegressionModel(1, 1).forward(np.array([[]])), np.array([]))==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):
      if x.size == 0:
          return np.array([])
      out = np.dot(x, self.weights) + self.bias
      return out