code: def _auto_placeholder(df, col_name, tf_name, block):
      info = _java_api().extra_schema_info(df._jdf)
      col_shape = [x.shape() for x in info if x.fieldName() == col_name]
      if len(col_shape) == 0:
          raise Exception("Could not find column with name {col_name}")
      col_shape = col_shape[0]
      col_struct = [x for x in df.schema.fields if x.name == col_name]
      if len(col_struct) == 0:
          raise
      col_struct = col_struct[0]
      # The dtypes known to TensorFrames.
      tfdtype = _get_dtype(col_struct.dataType)
      if tf_name is None:
          tf_name = col_name
      # Use the python convention (None)
      shape = [x if x >= 0 else None for x in col_shape]
      if not block:
          shape = shape[1:]
      else:
          # The lead is always set to None, because otherwise it may choke on empty partitions.
          # (This happens when the dataset is too small)
          # TODO(tjh) add a test for this case.
          shape[0] = None
      return tf.placeholder(tfdtype, shape=shape, name=tf_name)
  
imports: import tensorflow
  import numpy
  import logging
  import tempfile
  import pandas
  from pyspark import RDD, SparkContext
  from pyspark.sql import SQLContext, Row, DataFrame
  from pyspark.sql.types import DoubleType, IntegerType, LongType, FloatType, ArrayType
label_desc: Use image node_id 24 for calling a global function with example usage: _auto_placeholder(df, col_name, tf_name, block) and returns: tf