input: Write a function to create a neural network class in Python that models a linear regression function. The class should include an initialization method to set the weights and bias, and a forward method to compute the output of the model. The forward method should take an input array `x` and return the output. If the input or output dimensions are not specified, the function should raise an error.
  
  assert LinearRegressionModel(2, 1)
  assert LinearRegressionModel(3, 2)
  try:
      LinearRegressionModel()
      assert False, "Should raise an error"
  except TypeError:
      pass
  try:
      LinearRegressionModel(2)
      assert False, "Should raise an error"
  except TypeError:
      pass
output: class LinearRegressionModel:
      def __init__(self, input_dim, output_dim):
          if input_dim is None or output_dim is None:
              raise TypeError("Input or output dimensions are not specified")
          self.weights = [[0] * output_dim for _ in range(input_dim)]
          self.bias = [0] * output_dim
  
      def forward(self, x):
          if len(x) != len(self.weights):
              raise ValueError("Input dimension does not match the model's input dimension")
          output = [0] * len(self.bias)
          for i in range(len(x)):
              for j in range(len(self.bias)):
                  output[j] += x[i] * self.weights[i][j]
          for j in range(len(self.bias)):
              output[j] += self.bias[j]
          return output