old_contents: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from onnx_coreml import convert
  
  # Step 0 - (a) Define ML Model
  class small_model(nn.Module):
      def __init__(self):
          super(small_model, self).__init__()
          self.fc1 = nn.Linear(768, 256)
          self.fc2 = nn.Linear(256, 10)
  
      def forward(self, x):
          y = F.relu(self.fc1(x))
          y = F.softmax(self.fc2(y))
          return y
  
  # Step 0 - (b) Create model or Load from dist
  model = small_model()
  dummy_input = torch.randn(768)
  
  # Step 1 - PyTorch to ONNX model
  torch.onnx.export(model, dummy_input, './small_model.onnx')
  
  # Step 2 - ONNX to CoreML model
  mlmodel = convert(model='./small_model.onnx', target_ios='13')
  # Save converted CoreML model
  mlmodel.save('small_model.mlmodel')
  
new_contents: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from onnx_coreml import convert
  
  # Step 0 - (a) Define ML Model
  class small_model(nn.Module):
      def __init__(self):
          super(small_model, self).__init__()
          self.fc1 = nn.Linear(768, 256)
          self.fc2 = nn.Linear(256, 10)
  
      def forward(self, x):
          y = F.relu(self.fc1(x))
          y = F.softmax(self.fc2(y))
          return y
  
  # Step 0 - (b) Create model or Load from dist
  model = small_model()
  dummy_input = torch.randn(768)
  
  # Step 1 - PyTorch to ONNX model
  torch.onnx.export(model, dummy_input, './small_model.onnx')
  
  # Step 2 - ONNX to CoreML model
  mlmodel = convert(model='./small_model.onnx', minimum_ios_deployment_target='13')
  # Save converted CoreML model
  mlmodel.save('small_model.mlmodel')
  
subject: Update the example with latest interface