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 up the weights and bias, and a forward pass method to compute the output given input data. If the number of input features or outputs is not provided, default to 1.
  
  assert LinearRegressionModel(2, 1).weights.shape==(2, 1)
  assert LinearRegressionModel(3, 4).bias.shape==(1, 4)
  assert LinearRegressionModel(1, 1).forward(np.array([[2]])).shape==(1, 1)
output: 
  class LinearRegressionModel:
      def __init__(self, input_features=1, output_features=1):
          self.weights = np.random.randn(input_features, output_features)
          self.bias = np.random.randn(1, output_features)
  
      def forward(self, x):
          return np.dot(x, self.weights) + self.bias
  