code: def wrap_arrays(old, new):
      """Loop through an object's symbol table,
      wrapping each function with :func:`~pennylane.numpy.tensor_wrapper`.
  
      This is useful if you would like to wrap **every** function
      provided by an imported module.
  
      Args:
          old (dict): The symbol table to be wrapped. Note that
              callable classes are ignored; only functions are wrapped.
          new (dict): The symbol table that contains the wrapped values.
  
      .. seealso:: :func:`~pennylane.numpy.tensor_wrapper`
  
      **Example**
  
      This function is used to wrap the imported ``autograd.numpy``
      module, to enable all functions to support ``requires_grad``
      arguments, and to output :class:`~pennylane.numpy.tensor` objects:
  
      >>> from autograd import numpy as _np
      >>> wrap_arrays(_np.__dict__, globals())
      """
      for name, obj in old.items():
          if callable(obj) and not isinstance(obj, type):
              new[name] = tensor_wrapper(obj)
  
imports: from collections.abc import Sequence
  import functools
  from autograd import numpy
  from .tensor import tensor
label_desc: Use image node_id 3 for calling a global function with example usage: wrap_arrays(old, new) without return types