code: def _fit_with(
      self,
      name: str,
      line: np.ndarray,
      dtype: DataTypes,
      custom_type: Optional[ColumnTypes],
  ) -> Tuple[ColumnTypes, int]:
      prefix = f"values in {dtype} column {name}"
      postfix = "It'll be marked as redundant."
      if dtype == DataTypes.FLOAT:
          threshold = self.config.all_close_threshold
          if line.max().item() - line.min().item() <= threshold:
              if self.is_local_rank_0:
                  print_warning(f"{prefix} are ALL CLOSE. {postfix}")
              return ColumnTypes.REDUNDANT, 0
          if np.all(line == 0.0):
              if self.is_local_rank_0:
                  print_warning(f"{prefix} are ALL ZERO. {postfix}")
              return ColumnTypes.REDUNDANT, 0
          if custom_type is None:
              return ColumnTypes.NUMERICAL, 0
          return custom_type, 0
      num_unique = len(np.unique(line))
      num_samples = len(line)
      if num_unique == 1:
          if self.is_local_rank_0:
              print_warning(f"{prefix} are ALL SAME. {postfix}")
          return ColumnTypes.REDUNDANT, num_unique
      if num_unique == num_samples:
          if self.is_local_rank_0:
              print_warning(f"{prefix} are ALL DIFFERENT. {postfix}")
          return ColumnTypes.REDUNDANT, num_unique
      unique_ratio = num_unique / num_samples
      if unique_ratio >= self.config.redundancy_threshold:
          if self.is_local_rank_0:
              msg = f"{prefix} are TOO MANY (ratio={unique_ratio:8.6f}). {postfix}"
              print_warning(msg)
          return ColumnTypes.REDUNDANT, num_unique
      if custom_type is None:
          return ColumnTypes.CATEGORICAL, num_unique
      return custom_type, num_unique
  
imports: import numpy
  from typing import Any
  from typing import Dict
  from typing import Tuple
  from typing import Optional
  from dataclasses import dataclass
  from cftool.misc import print_warning
  from .file import FileParserBlock
  from ....schema import DataTypes
  from ....schema import DataBundle
  from ....schema import ColumnTypes
  from ....schema import INoInitDataBlock
label_desc: Use image node_id 6 for calling the RecognizerBlock obj's underlying member method code with example usage: obj._fit_with(name, line, dtype, custom_type) and returns: custom_type,  num_unique, custom_type,  int, ColumnTypes,  num_unique, ColumnTypes,  num_unique, ColumnTypes,  num_unique, ColumnTypes,  num_unique, ColumnTypes,  int, ColumnTypes,  int, ColumnTypes,  int