code: def is_hat_shaped(learning_rates: List[float]):
      """
      Check if the list of learning rates is "hat" shaped, i.e.,
      increases then decreases
      """
      # sufficient conditions:
      #   has both an increasing and decreasing segment
      #   decrease segment occurs after increasing segment
      #   once start decreasing, can't increase again
      has_increasing_segment = False
      has_decreasing_segment = False
      for k in range(1, len(learning_rates)):
          delta = learning_rates[k] - learning_rates[k - 1]
          if delta > 1e-8:
              has_increasing_segment = True
              if has_decreasing_segment:
                  # can't increase again after hitting the max
                  return False
          elif delta < -1e-8:
              if not has_increasing_segment:
                  # can't decrease without have an increasing segment
                  return False
              has_decreasing_segment = True
          else:
              # no change
              pass
  
      return has_increasing_segment and has_decreasing_segment
  
imports: from collections import OrderedDict
  from copy import deepcopy
  from typing import Any, Dict, List, Tuple
  import torch
  import pytest
  from allennlp.common import Lazy, Params
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.testing import AllenNlpTestCase
  from allennlp.data.data_loaders import SimpleDataLoader
  from allennlp.training import Trainer
  from allennlp.training.learning_rate_schedulers import LearningRateScheduler, SlantedTriangular
  from allennlp.training.optimizers import Optimizer
label_desc: Use image node_id 1 for calling a global function with example usage: is_hat_shaped(learning_rates) and returns: unknown, False, False