input: Write a python function to initialize an artificial neural network (ANN) using PyTorch and test its forward pass with random input data.
  assert isinstance(forward_pass(torch.randn(1, 50)), torch.Tensor)
  assert forward_pass(torch.randn(1, 50)).shape == (1, 3)
  assert forward_pass(torch.randn(10, 50)).shape == (10, 3)
output: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  class SimpleANN(nn.Module):
      def __init__(self, input_size, hidden_size, output_size):
          super(SimpleANN, self).__init__()
          self.fc1 = nn.Linear(input_size, hidden_size)
          self.fc2 = nn.Linear(hidden_size, output_size)
  
      def forward(self, x):
          x = F.relu(self.fc1(x))
          x = self.fc2(x)
          return x
  
  def initialize_ann():
      input_size = 50
      hidden_size = 20
      output_size = 3
      model = SimpleANN(input_size, hidden_size, output_size)
      return model
  
  def forward_pass(input_data):
      model = initialize_ann()
      return model(input_data)