optimization_profile.py

    # Query input names and shapes from parsed TensorRT network
    network_inputs = [network.get_input(i) for i in range(network.num_inputs)]
    input_names = [_input.name for _input in network_inputs]   # ex: ["actual_input1"]
    # Note the original model must have dynamic (-1) dimensions for variable min/opt/max values 
    # in your profile dimensions such as the batch dimension in this example
    input_shapes = [_input.shape for _input in network_inputs] # ex: [(-1, 3, 224, 224)]
    max_batch_size = 64
    
    # Create optimization profile for dynamic batch dimension
    profile0 = builder.create_optimization_profile()
    for name, shape in zip(input_names, input_shapes):
        profile0.set_shape(
          name, min=(1, *shape[1:]), opt=(max_batch_size, *shape[1:]), max=(max_batch_size, *shape[1:])
        )
    config.add_optimization_profile(profile0)