code: def parse_shape_string(inputs_string):
      """Parse an input shape dictionary string to a usable dictionary.
  
      Parameters
      ----------
      inputs_string: str
          A string of the form "input_name:[dim1,dim2,...,dimn] input_name2:[dim1,dim2]" that
          indicates the desired shape for specific model inputs. Colons, forward slashes and dots
          within input_names are supported. Spaces are supported inside of dimension arrays.
  
      Returns
      -------
      shape_dict: dict
          A dictionary mapping input names to their shape for use in relay frontend converters.
      """
  
      # Create a regex pattern that extracts each separate input mapping.
      # We want to be able to handle:
      # * Spaces inside arrays
      # * forward slashes inside names (but not at the beginning or end)
      # * colons inside names (but not at the beginning or end)
      # * dots inside names
      pattern = r"(?:\w+\/)?[:\w.]+\:\s*\[\-?\d+(?:\,\s*\-?\d+)*\]"
      input_mappings = re.findall(pattern, inputs_string)
      if not input_mappings:
          raise argparse.ArgumentTypeError(
              "--input-shapes argument must be of the form "
              '"input_name:[dim1,dim2,...,dimn] input_name2:[dim1,dim2]"'
          )
      shape_dict = {}
      for mapping in input_mappings:
          # Remove whitespace.
          mapping = mapping.replace(" ", "")
          # Split mapping into name and shape.
          name, shape_string = mapping.rsplit(":", 1)
          # Convert shape string into a list of integers or Anys if negative.
          shape = [
              int(x) if int(x) > 0 else relay.Any()
              for x in shape_string.strip("][").split(",")
          ]
          # Add parsed mapping to shape dictionary.
          shape_dict[name] = shape
  
      return shape_dict
  
imports: import argparse
  import re
  from tvm import relay
label_desc: Use image node_id 1 for calling a global function with example usage: parse_shape_string(inputs_string) and returns: shape_dict