class_docstr: Represents a framework-agnostic neural network layer in a directed graph.
class_docstr_tok: ["Represents","a","framework-agnostic","neural","network","layer","in","a","directed","graph","."]
code: def title(self):
      # Default
      title = self.name or self.op
  
      if self.op == "Dropout":
          if "ratio" in self.params:
              title += " " + str(self.params["ratio"])
  
      if "kernel_shape" in self.params:
          # Kernel
          kernel = self.params["kernel_shape"]
          title += "x".join(map(str, kernel))
      if "stride" in self.params:
          stride = self.params["stride"]
          if np.unique(stride).size == 1:
              stride = stride[0]
          if stride != 1:
              title += "/s{}".format(str(stride))
      #         # Transposed
      #         if node.transposed:
      #             name = "Transposed" + name
      return title
  
imports: from __future__ import absolute_import, division, print_function
  import os
  import re
  from random import getrandbits
  import inspect
  import numpy
  import html
label_desc: Use image node_id 2 for calling the Node obj's underlying member method code with example usage: obj.title() and returns: title