The error encountered when trying to call NumPy functions on a tensor that requires gradients typically arises from the incompatibility between PyTorch’s autograd system and NumPy. Tensors with gradients are part of a computational graph that tracks operations for the purpose of automatic differentiation. When you attempt to convert such tensors to NumPy arrays using `.numpy()`, it raises an error because the gradient tracking mechanism is incompatible with NumPy’s static data structure. Essentially, PyTorch’s tensors maintain an additional state to track gradients, which NumPy cannot recognize or handle, leading to a conflict.

To circumvent this issue, you can detach the tensor from the computation graph using the `.detach()` method before performing any NumPy operations. This will create a new tensor that does not require gradients, allowing for seamless conversion to a NumPy array. For instance, if `x` is a tensor that requires gradients, you should execute `x.detach().numpy()` for further processing with NumPy. This approach not only preserves the original tensor’s gradient information but also enables you to leverage NumPy functions for operations that do not require gradient tracking, thereby maintaining an efficient workflow in your deep learning projects.