code: def infer_problem_type(y: Series, silent=False) -> str:
      """Identifies which type of prediction problem we are interested in (if user has not specified).
      Ie. binary classification, multi-class classification, or regression.
      """
      # treat None, NaN, INF, NINF as NA
      y = y.replace([np.inf, -np.inf], np.nan, inplace=False)
      y = y.dropna()
      num_rows = len(y)
  
      if num_rows == 0:
          raise ValueError("Label column cannot have 0 valid values")
  
      unique_values = y.unique()
  
      if num_rows > LARGE_DATA_THRESHOLD:
          regression_threshold = REGRESS_THRESHOLD_LARGE_DATA  # if the unique-ratio is less than this, we assume multiclass classification, even when labels are integers
      else:
          regression_threshold = REGRESS_THRESHOLD_SMALL_DATA
  
      unique_count = len(unique_values)
      if unique_count == 2:
          problem_type = BINARY
          reason = "only two unique label-values observed"
      elif y.dtype.name in ["object", "category", "string"]:
          problem_type = MULTICLASS
          reason = f"dtype of label-column == {y.dtype.name}"
      elif np.issubdtype(y.dtype, np.floating):
          unique_ratio = unique_count / float(num_rows)
          if (unique_ratio <= regression_threshold) and (
              unique_count <= MULTICLASS_UPPER_LIMIT
          ):
              try:
                  can_convert_to_int = np.array_equal(y, y.astype(int))
                  if can_convert_to_int:
                      problem_type = MULTICLASS
                      reason = "dtype of label-column == float, but few unique label-values observed and label-values can be converted to int"
                  else:
                      problem_type = REGRESSION
                      reason = "dtype of label-column == float and label-values can't be converted to int"
              except:
                  problem_type = REGRESSION
                  reason = "dtype of label-column == float and label-values can't be converted to int"
          else:
              problem_type = REGRESSION
              reason = "dtype of label-column == float and many unique label-values observed"
      elif np.issubdtype(y.dtype, np.integer):
          unique_ratio = unique_count / float(num_rows)
          if (unique_ratio <= regression_threshold) and (
              unique_count <= MULTICLASS_UPPER_LIMIT
          ):
              problem_type = MULTICLASS  # TODO: Check if integers are from 0 to n-1 for n unique values, if they have a wide spread, it could still be regression
              reason = "dtype of label-column == int, but few unique label-values observed"
          else:
              problem_type = REGRESSION
              reason = "dtype of label-column == int and many unique label-values observed"
      else:
          raise NotImplementedError(
              f"label dtype {y.dtype} not supported!"
          )
      if not silent:
          logger.log(
              25,
              f"AutoGluon infers your prediction problem is: '{problem_type}' (because {reason}).",
          )
  
          # TODO: Move this outside of this function so it is visible even if problem type was not inferred.
          if problem_type in [BINARY, MULTICLASS]:
              if unique_count > 10:
                  logger.log(
                      20,
                      f"\tFirst 10 (of {unique_count}) unique label values:  {list(unique_values[:10])}",
                  )
              else:
                  logger.log(
                      20,
                      f"\t{unique_count} unique label values:  {list(unique_values)}",
                  )
          elif problem_type == REGRESSION:
              y_max = y.max()
              y_min = y.min()
              y_mean = y.mean()
              y_stddev = y.std()
              logger.log(
                  20,
                  f"\tLabel info (max, min, mean, stddev): ({y_max}, {y_min}, {round(y_mean, 5)}, {round(y_stddev, 5)})",
              )
  
          logger.log(
              25,
              f"\tIf '{problem_type}' is not the correct problem_type, please manually specify the problem_type parameter during predictor init "
              f"(You may specify problem_type as one of: {[BINARY, MULTICLASS, REGRESSION]})",
          )
      return problem_type
  
imports: import logging
  import math
  import pickle
  import random
  import sys
  import time
  from typing import Callable, List, Tuple, Union
  import numpy
  import pandas
  import scipy.stats
  from numpy.typing import ArrayLike
  from pandas import DataFrame, Series
  from sklearn.model_selection import LeaveOneGroupOut, RepeatedKFold, RepeatedStratifiedKFold, train_test_split
  from autogluon.common.utils.resource_utils import ResourceManager
  from ..constants import BINARY, LARGE_DATA_THRESHOLD, MULTICLASS, MULTICLASS_UPPER_LIMIT, QUANTILE, REGRESS_THRESHOLD_LARGE_DATA, REGRESS_THRESHOLD_SMALL_DATA, REGRESSION, SOFTCLASS
  from ..metrics import Scorer, accuracy, pinball_loss, root_mean_squared_error
  from .miscs import warning_filter
label_desc: Use image node_id 16 for calling a global function with example usage: infer_problem_type(y, silent) and returns: problem_type