class_docstr: Default data parser for CSVDataset. It
  1. ignores any columns which does not have a header.
  2. tries to convert to list of numeric values(generated by
      np.array().tolist()) if cell data is a str separated by ','.
  3. read data and infer data type directly, otherwise.
class_docstr_tok: ["Default","data","parser","for","CSVDataset",".","It","1.","ignores","any","columns","which","does","not","have","a","header",".","2.","tries","to","convert","to","list","of","numeric","values","(","generated","by","np.array","(",")",".tolist","(",")",")","if","cell","data","is","a","str","separated","by","'",",","'",".","3.","read","data","and","infer","data","type","directly",",","otherwise","."]
code: def __call__(self, df: pd.DataFrame):
      data = {}
      for header in df:
          if "Unnamed" in header:
              dgl_warning("Unnamed column is found. Ignored...")
              continue
          dt = df[header].to_numpy().squeeze()
          if len(dt) > 0 and isinstance(dt[0], str):
              # probably consists of list of numeric values
              dt = np.array([ast.literal_eval(row) for row in dt])
          data[header] = dt
      return data
  
imports: import ast
  import os
  from typing import Callable, List, Optional
  import numpy
  import pandas
  import pydantic
  import yaml
  from ..None import backend
  from ..base import dgl_warning, DGLError
  from ..convert import heterograph
label_desc: Use image node_id 1 for calling the DefaultDataParser obj's underlying member method code with example usage: obj.__call__(df) and returns: data