input: def LinearRegressionModel(input_dim: int, output_dim: int) -> str:
      """ Create a neural network class in Python to model a linear regression function.
      The class should initialize weights and bias, and implement a forward method to
      calculate the output of the model.
      >>> LinearRegressionModel(2, 1) == "import numpy as np\nclass LinearRegressionModel:\n    def __init__(self, input_dim, output_dim):\n        ''' Initialises the weights and bias of the linear regression model\n        Arguments:\n            input_dim {int} -- Number of input features\n            output_dim {int} -- Number of outputs\n        '''\n        self.weights = np.zeros((input_dim, output_dim))\n        self.bias = np.zeros((1, output_dim))\n\n    def forward(self, x):\n        ''' Calculates the forward pass of the linear regression model\n        Arguments:\n            x {numpy array} -- Input data of size (N, input_dim)\n        Returns:\n            out {numpy array} -- Output of the linear regression model of size (N, output_dim)\n        '''\n        out = np.dot(x, self.weights) + self.bias\n        return out"
      True
      >>> LinearRegressionModel(3, 2) == "import numpy as np\nclass LinearRegressionModel:\n    def __init__(self, input_dim, output_dim):\n        ''' Initialises the weights and bias of the linear regression model\n        Arguments:\n            input_dim {int} -- Number of input features\n            output_dim {int} -- Number of outputs\n        '''\n        self.weights = np.zeros((input_dim, output_dim))\n        self.bias = np.zeros((1, output_dim))\n\n    def forward(self, x):\n        ''' Calculates the forward pass of the linear regression model\n        Arguments:\n            x {numpy array} -- Input data of size (N, input_dim)\n        Returns:\n            out {numpy array} -- Output of the linear regression model of size (N, output_dim)\n        '''\n        out = np.dot(x, self.weights) + self.bias\n        return out"
      True
      """
output:     return f"import numpy as np\nclass LinearRegressionModel:\n    def __init__(self, input_dim, output_dim):\n        ''' Initialises the weights and bias of the linear regression model\n        Arguments:\n            input_dim {{int}} -- Number of input features\n            output_dim {{int}} -- Number of outputs\n        '''\n        self.weights = np.zeros((input_dim, output_dim))\n        self.bias = np.zeros((1, output_dim))\n\n    def forward(self, x):\n        ''' Calculates the forward pass of the linear regression model\n        Arguments:\n            x {{numpy array}} -- Input data of size (N, input_dim)\n        Returns:\n            out {{numpy array}} -- Output of the linear regression model of size (N, output_dim)\n        '''\n        out = np.dot(x, self.weights) + self.bias\n        return out"