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 forward pass of the linear regression model. If the input or output dimensions are invalid, the function should return an error message.
  
  assert LinearRegressionModel(3, 1).__init__(3, 1) is None
  assert LinearRegressionModel(3, 1).forward(np.array([[1, 2, 3]])) is not None
  assert LinearRegressionModel(-1, 1).__init__(-1, 1) == "Invalid dimensions"
  assert LinearRegressionModel(3, -1).forward(np.array([[1, 2, 3]])) == "Invalid dimensions"
output: class LinearRegressionModel:
      def __init__(self, input_dim, output_dim):
          if input_dim <= 0 or output_dim <= 0:
              return "Invalid dimensions"
          self.weights = np.zeros((input_dim, output_dim))
          self.bias = np.zeros(output_dim)
  
      def forward(self, x):
          if x.shape[1] != self.weights.shape[0]:
              return "Invalid dimensions"
          return np.dot(x, self.weights) + self.bias