input: Write a function to implement a Linear Regression Model in Python using a neural network approach. The class should initialize weights and bias, and should have a method to compute the forward pass. If the input data array x has dimensions (N, input_dim) and the output should have dimensions (N, output_dim), return the output.
  
  
  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(3, 2)
  input_data = np.array([[1, 2, 3], [4, 5, 6]])
  output_data = model.forward(input_data)
  print(output_data)
  
  
  Expected output:
  
  [[0. 0.]
   [0. 0.]]
  
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