input: Write a function in Python that creates a neural network class to model a linear regression function. The class should have an initialization method that sets up the weights and bias for the model. It should also have a forward method that computes the output of the linear regression model given input data.
  assert LinearRegressionModel(input_dim=2, output_dim=1)
  assert LinearRegressionModel(input_dim=3, output_dim=2)
  assert LinearRegressionModel(input_dim=4, output_dim=4)
  assert isinstance(LinearRegressionModel(5, 5).forward(np.array([[1,1,1,1,1], [2,2,2,2,2]])))
  assert LinearRegressionModel(input_dim=0, output_dim=0)
output: 
  import numpy as np
  
  class LinearRegressionModel:
      def __init__(self, input_dim, output_dim):
          self.weights = np.random.randn(input_dim, output_dim)
          self.bias = np.random.randn(output_dim)
      
      def forward(self, x):
          return np.dot(x, self.weights) + self.bias
  