class_docstr: Block Decoder for readability.
class_docstr_tok: ["Block","Decoder","for","readability","."]
docstr: Gets a block through a string notation of arguments.
docstr_tok: ["Gets","a","block","through","a","string","notation","of","arguments","."]
code: def _decode_block_string(self, block_string):
      """Gets a block through a string notation of arguments."""
      if six.PY2:
          assert isinstance(block_string, (str, unicode))
      else:
          assert isinstance(block_string, str)
      ops = block_string.split("_")
      options = {}
      for op in ops:
          splits = re.split(r"(\d.*)", op)
          if len(splits) >= 2:
              key, value = splits[:2]
              options[key] = value
  
      if "s" not in options or len(options["s"]) != 2:
          raise ValueError(
              "Strides options should be a pair of integers."
          )
  
      return efficientnet_model.BlockArgs(
          kernel_size=int(options["k"]),
          num_repeat=int(options["r"]),
          input_filters=int(options["i"]),
          output_filters=int(options["o"]),
          expand_ratio=int(options["e"]),
          id_skip=("noskip" not in block_string),
          se_ratio=float(options["se"]) if "se" in options else None,
          strides=[int(options["s"][0]), int(options["s"][1])],
          conv_type=int(options["c"]) if "c" in options else 0,
          fused_conv=int(options["f"]) if "f" in options else 0,
          super_pixel=int(options["p"]) if "p" in options else 0,
          condconv=("cc" in block_string),
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import os
  import re
  from absl import logging
  import numpy
  import six
  import tensorflow.compat.v1
  import efficientnet_model
  import utils
label_desc: Use image node_id 1 for calling the BlockDecoder obj's underlying member method code with example usage: obj._decode_block_string(block_string) and returns: efficientnet_model