class_docstr: Creates new connection to Salesforce and allows you to pull data out of SFDC and save it to a file.
  
  You can then use that file with other Airflow operators to move the data into another data source.
  
  :param conn_id: The name of the connection that has the parameters needed to connect to Salesforce.
      The connection should be of type `Salesforce`.
  :param session_id: The access token for a given HTTP request session.
  :param session: A custom HTTP request session. This enables the use of requests Session features not
      otherwise exposed by `simple_salesforce`.
  
  .. note::
      A connection to Salesforce can be created via several authentication options:
  
      * Password: Provide Username, Password, and Security Token
      * Direct Session: Provide a `session_id` and either Instance or Instance URL
      * OAuth 2.0 JWT: Provide a Consumer Key and either a Private Key or Private Key File Path
      * IP Filtering: Provide Username, Password, and an Organization ID
  
      If in sandbox, enter a Domain value of 'test'.
docstr: Convert a column of a dataframe to UNIX timestamps if applicable.
  
  :param column: A Series object representing a column of a dataframe.
  :return: a new series that maintains the same index as the original
docstr_tok: ["Convert","a","column","of","a","dataframe","to","UNIX","timestamps","if","applicable",".",":","param","column",":","A","Series","object","representing","a","column","of","a","dataframe",".",":","return",":","a","new","series","that","maintains","the","same","index","as","the","original"]
code: def _to_timestamp(cls, column: pd.Series) -> pd.Series:
      """
      Convert a column of a dataframe to UNIX timestamps if applicable.
  
      :param column: A Series object representing a column of a dataframe.
      :return: a new series that maintains the same index as the original
      """
      # try and convert the column to datetimes
      # the column MUST have a four digit year somewhere in the string
      # there should be a better way to do this,
      # but just letting pandas try and convert every column without a format
      # caused it to convert floats as well
      # For example, a column of integers
      # between 0 and 10 are turned into timestamps
      # if the column cannot be converted,
      # just return the original column untouched
      import numpy as np
      import pandas as pd
  
      try:
          column = pd.to_datetime(column)
      except ValueError:
          log.error(
              "Could not convert field to timestamps: %s", column.name
          )
          return column
  
      # now convert the newly created datetimes into timestamps
      # we have to be careful here
      # because NaT cannot be converted to a timestamp
      # so we have to return NaN
      converted = []
      for value in column:
          try:
              converted.append(value.timestamp())
          except (ValueError, AttributeError):
              converted.append(np.NaN)
  
      return pd.Series(converted, index=column.index)
  
imports: from __future__ import annotations
  import logging
  import time
  from functools import cached_property
  from typing import TYPE_CHECKING, Any, Iterable
  from simple_salesforce import Salesforce, api
  from airflow.hooks.base import BaseHook
label_desc: Use image node_id 11 for calling the SalesforceHook obj's underlying member method code with example usage: obj._to_timestamp(cls, column) and returns: pd, column