class_docstr: Class that recognizes the type of given file and provides with an
  adequate loader.
  
  Properties
  ----------
  supported_types: dict
       Dictionary of supported types along with their adequate loaders, to support a new data type, this
       dictionary needs to be updated with the file extension as key and the loading function name as value.
  
  Example
  -------
  >>>identifier = DataSourceIdentifier("data.csv")
  >>>loader = identifier.fetch_loader()
  >>>X = loader("data.csv")
class_docstr_tok: ["Class","that","recognizes","the","type","of","given","file","and","provides","with","an","adequate","loader",".","Properties","--","--","--","--","--","supported_types",":","dict","Dictionary","of","supported","types","along","with","their","adequate","loaders",",","to","support","a","new","data","type",",","this","dictionary","needs","to","be","updated","with","the","file","extension","as","key","and","the","loading","function","name","as","value",".","Example","--","--","--","-",">",">",">","identifier","=","DataSourceIdentifier","(","``","data.csv","''",")",">",">",">","loader","=","identifier.fetch_loader","(",")",">",">",">","X","=","loader","(","``","data.csv","''",")"]
docstr: Returns identified source type.
docstr_tok: ["Returns","identified","source","type","."]
code: def get_src(self):
      """Returns identified source type."""
      return self.src
  
file_path: AmpliGraph/ampligraph/datasets/source_identifier.py
imports: import logging
  from collections.abc import Iterable
  from itertools import chain, islice
  import numpy
  import pandas
label_desc: Use image node_id 3 for calling the DataSourceIdentifier obj's underlying member method code with example usage: obj.get_src() and returns: self