original_docstring: 
      Returns the tensorflow data type that corresponds to an the data type specified by the parameter.
      The returned type is the type that must be used to parse a feature of 'data_type' that has been converted
      with _auto_feature() (see above).
  
      :param data_type: Python native type or numpy type that is to be converted to tensorflow type.
      :type data_type: type
      :raise ValueError: When the specified data type is not recognised as a type that has a tensorflow equivalent.
      :return: Tensorflow type that corresponds to the specified data type.
      :rtype: tensorflow.python.framework.dtypes.DType
      
docstring: Returns the tensorflow data type that corresponds to an the data type specified by the parameter.
  The returned type is the type that must be used to parse a feature of 'data_type' that has been converted
  with _auto_feature() .
docstring_tokens: Returns the tensorflow data type that corresponds to an the data type specified by the parameter . The returned type is the type that must be used to parse a feature of ' data_type ' that has been converted with _auto_feature () .
code: def _auto_tf_type(data_type):
      """
      Returns the tensorflow data type that corresponds to an the data type specified by the parameter.
      The returned type is the type that must be used to parse a feature of 'data_type' that has been converted
      with _auto_feature() (see above).
  
      :param data_type: Python native type or numpy type that is to be converted to tensorflow type.
      :type data_type: type
      :raise ValueError: When the specified data type is not recognised as a type that has a tensorflow equivalent.
      :return: Tensorflow type that corresponds to the specified data type.
      :rtype: tensorflow.python.framework.dtypes.DType
      """
      if data_type in (np.float32, np.float64, float):
          tf_type = tf.float32
      elif data_type in (int, bool, np.uint32, np.uint64, np.int64):
          tf_type = tf.int64
      elif data_type in (str, bytes):
          tf_type = tf.string
      else:
          raise ValueError(f'Unrecognised data_type {data_type}')
  
      return tf_type
code_tokens: def _auto_tf_type ( data_type ) : if data_type in ( np . float32 , np . float64 , float ) : tf_type = tf . float32 elif data_type in ( int , bool , np . uint32 , np . uint64 , np . int64 ) : tf_type = tf . int64 elif data_type in ( str , bytes ) : tf_type = tf . string else : raise ValueError ( f'Unrecognised data_type {data_type}' ) return tf_type
short_docstring: Returns the tensorflow data type that corresponds to an the data type specified by the parameter.
short_docstring_tokens: Returns the tensorflow data type that corresponds to an the data type specified by the parameter .
comment: """
      Returns the tensorflow data type that corresponds to an the data type specified by the parameter.
      The returned type is the type that must be used to parse a feature of 'data_type' that has been converted
      with _auto_feature() (see above).
  
      :param data_type: Python native type or numpy type that is to be converted to tensorflow type.
      :type data_type: type
      :raise ValueError: When the specified data type is not recognised as a type that has a tensorflow equivalent.
      :return: Tensorflow type that corresponds to the specified data type.
      :rtype: tensorflow.python.framework.dtypes.DType
      """