code: def check_column(
      df: DataFrame, row: int, name: str, fn: Callable[[float], bool]
  ) -> bool:
      """Checks the values of a column using a custom function and logs abnormals.
  
      The check is only performed on TensorRT models, not native CPU/GPU models.
  
      Args:
        df: The DataFrame to be checked.
        row: The row in the DataFrame
        name: The name of the column to be checked.
        fn: The function that takes a value of at the specified column and returns
          if the value satisfies the check.
  
      Returns:
        Whether all the values of the specified column satisfies the provided check.
      """
      is_ok = True
      if df(row, "trt_model"):
          if not fn(df(row, name)):
              logging.error(
                  "Unsatisfied %s found at: %s", name, df(row)
              )
              is_ok = False
      return is_ok
  
imports: import itertools
  import json
  from typing import Any, Callable, Optional, Sequence, Tuple, Union
  from collections import namedtuple
  import numpy
  from tensorflow.python.compiler.tensorrt.model_tests import model_handler
  import tensorflow.python.compiler.tensorrt.trt_convert
  from tensorflow.python.platform import tf_logging
label_desc: Use image node_id 5 for calling a global function with example usage: check_column(df, row, name, fn) and returns: is_ok