class_docstr: A simple wrapper to easily create "linear" graph,
  consisting of layers / symbolic functions with only one input & output.
class_docstr_tok: ["A","simple","wrapper","to","easily","create","``","linear","''","graph",",","consisting","of","layers","\/","symbolic","functions","with","only","one","input","&","output","."]
docstr: Apply a function on the wrapped tensor.
  
  Returns:
      LinearWrap: ``LinearWrap(func(self.tensor(), *args, **kwargs))``.
docstr_tok: ["Apply","a","function","on","the","wrapped","tensor",".","Returns",":","LinearWrap",":","``","LinearWrap","(","func","(","self.tensor","(",")",",","*","args",",","*","*","kwargs",")",")","``","."]
code: def apply(self, func, *args, **kwargs):
      """
      Apply a function on the wrapped tensor.
  
      Returns:
          LinearWrap: ``LinearWrap(func(self.tensor(), *args, **kwargs))``.
      """
      ret = func(self._t, *args, **kwargs)
      return LinearWrap(ret)
  
file_path: tensorpack/tensorpack/models/linearwrap.py
imports: from types import ModuleType
  import six
  from .registry import get_registered_layer
label_desc: Use image node_id 3 for calling the LinearWrap obj's underlying member method code with example usage: obj.apply(func) and returns: LinearWrap