Although TensorFlow's static computational graph offers performance optimizations, such as graph optimization and model parallelism, PyTorch's dynamic graph gives you more control over the execution flow. This control is beneficial for applications that require dynamic behavior, such as recurrent neural networks or attention mechanisms.

PyTorch uses a dynamic computational graph, which allows for easy use of control flow structures like loops and conditionals. This enables more flexibility and easier debugging. In contrast, TensorFlow uses a static computational graph, which requires pre-defining the graph structure before execution. This makes TensorFlow more suitable for deployment and optimization.

PyTorch's dynamic computational graph allows for easy model building and experimentation, as it allows us to define and modify the computation graph on the fly. TensorFlow's static graph, on the other hand, requires redefining the entire graph for any modification, making iterative development more cumbersome.

The dynamic nature of PyTorch's computational graph makes it simpler to implement complex models that have varying structures at runtime. This is particularly useful in scenarios like natural language processing, where the structure of the model may depend on the input sequence length. With TensorFlow's static graph, you would need to choose a fixed input sequence length and pad/truncate other sequences to match it, which can be less efficient.