class_docstr: Serialize data of various formats to a LibSVM-formatted string.
  
  The data must already be in LIBSVM file format:
  <label> <index1>:<value1> <index2>:<value2> ...
  
  It is suitable for sparse datasets since it does not store zero-valued
  features.
docstr: Serialize data of various formats to a LibSVM-formatted string.
  
  Args:
      data (object): Data to be serialized. Can be a string or a
          file-like object.
  
  Returns:
      str: The data serialized as a LibSVM-formatted string.
  
  Raises:
      ValueError: If unable to handle input format
docstr_tok: ["Serialize","data","of","various","formats","to","a","LibSVM-formatted","string",".","Args",":","data","(","object",")",":","Data","to","be","serialized",".","Can","be","a","string","or","a","file-like","object",".","Returns",":","str",":","The","data","serialized","as","a","LibSVM-formatted","string",".","Raises",":","ValueError",":","If","unable","to","handle","input","format"]
code: def serialize(self, data):
      """Serialize data of various formats to a LibSVM-formatted string.
  
      Args:
          data (object): Data to be serialized. Can be a string or a
              file-like object.
  
      Returns:
          str: The data serialized as a LibSVM-formatted string.
  
      Raises:
          ValueError: If unable to handle input format
      """
  
      if isinstance(data, str):
          return data
  
      if hasattr(data, "read"):
          return data.read()
  
      raise ValueError("Unable to handle input format: %s" % type(data))
  
imports: from __future__ import absolute_import
  import abc
  from collections.abc import Iterable
  import csv
  import io
  import json
  import numpy
  from pandas import DataFrame
  from six import with_metaclass
  from sagemaker.utils import DeferredError
label_desc: Use image node_id 2 for calling the LibSVMSerializer obj's underlying member method code with example usage: obj.serialize(data) and returns: data, data