docstr: Gets the encoded data for any given column of the `EncodedDs`.
  
  :param column_name: name of the column.
  :return: A `torch.Tensor` with the encoded data of the `column_name` column.
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code: def get_encoded_column_data(self, column_name: str) -> torch.Tensor:
      """
      Gets the encoded data for any given column of the `EncodedDs`.
  
      :param column_name: name of the column.
      :return: A `torch.Tensor` with the encoded data of the `column_name` column.
      """
      if self.use_cache and self.cache_built:
          if column_name == self.target and self.Y_cache is not None:
              return self.Y_cache
          elif self.X_cache is not torch.nan:
              a, b = self.encoder_spans[column_name]
              return self.X_cache[:, a:b]
  
      kwargs = {}
      if (
          "dependency_data"
          in inspect.signature(
              self.encoders[column_name].encode
          ).parameters
      ):
          deps = [
              dep
              for dep in self.encoders[column_name].dependencies
              if dep in self.data_frame.columns
          ]
          kwargs["dependency_data"] = {
              dep: self.data_frame[dep] for dep in deps
          }
      encoded_data = self.encoders[column_name].encode(
          self.data_frame[column_name], **kwargs
      )
      if (
          torch.isnan(encoded_data).any()
          or torch.isinf(encoded_data).any()
      ):
          raise Exception(
              f"Encoded tensor: {encoded_data} contains nan or inf values"
          )
  
      if not isinstance(encoded_data, torch.Tensor):
          raise Exception(
              f"The encoder: {self.encoders[column_name]} for column: {column_name} does not return a Tensor!"
          )
  
      if self.use_cache and not self.cache_built:
          if column_name == self.target:
              self.Y_cache = encoded_data
          else:
              a, b = self.encoder_spans[column_name]
              self.X_cache = self.X_cache[:, a:b]
  
      return encoded_data
  
imports: import inspect
  from typing import List, Tuple, Dict
  import torch
  import numpy
  import pandas
  from torch.utils.data import Dataset
  from lightwood.encoder.base import BaseEncoder
label_desc: Use image node_id 6 for calling the EncodedDs obj's underlying member method code with example usage: obj.get_encoded_column_data(column_name) and returns: encoded_data, self, self