docstr: Tests to ensure all output sequences are greater than a specified minimum length.
  It uses the `take_short_sequence_step` step function, which favors shorter sequences.
  See `test_take_short_sequence_step`.
docstr_tok: ["Tests","to","ensure","all","output","sequences","are","greater","than","a","specified","minimum","length",".","It","uses","the","`","take_short_sequence_step","`","step","function",",","which","favors","shorter","sequences",".","See","`","test_take_short_sequence_step","`","."]
code: def test_min_steps(self):
      """
      Tests to ensure all output sequences are greater than a specified minimum length.
      It uses the `take_short_sequence_step` step function, which favors shorter sequences.
      See `test_take_short_sequence_step`.
      """
      self.beam_search.beam_size = 1
  
      # An empty sequence is allowed under this step function
      self.beam_search.min_steps = 0
      expected_top_k = np.array([[5]])
      expected_log_probs = np.log(np.array([0.9]))
      with pytest.warns(
          RuntimeWarning, match="Empty sequences predicted"
      ):
          self._check_results(
              expected_top_k=expected_top_k,
              expected_log_probs=expected_log_probs,
              take_step=take_short_sequence_step,
          )
  
      self.beam_search.min_steps = 1
      expected_top_k = np.array([[1, 5]])
      expected_log_probs = np.log(np.array([0.09]))
      self._check_results(
          expected_top_k=expected_top_k,
          expected_log_probs=expected_log_probs,
          take_step=take_short_sequence_step,
      )
  
      self.beam_search.min_steps = 2
      expected_top_k = np.array([[1, 2, 5]])
      expected_log_probs = np.log(np.array([0.009]))
      self._check_results(
          expected_top_k=expected_top_k,
          expected_log_probs=expected_log_probs,
          take_step=take_short_sequence_step,
      )
  
      self.beam_search.beam_size = 3
      self.beam_search.min_steps = 2
      expected_top_k = np.array(
          [[1, 2, 5, 5, 5], [1, 2, 3, 5, 5], [1, 2, 3, 4, 5]]
      )
      expected_log_probs = np.log(np.array([0.009, 0.0009, 0.0001]))
      self._check_results(
          expected_top_k=expected_top_k,
          expected_log_probs=expected_log_probs,
          take_step=take_short_sequence_step,
      )
  
imports: from typing import Dict, Tuple, Union
  import numpy
  import pytest
  import torch
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.testing import AllenNlpTestCase
  from allennlp.nn.beam_search import MultinomialSampler, BeamSearch, TopKSampler, TopPSampler, GumbelSampler, LengthNormalizedSequenceLogProbabilityScorer, RepeatedNGramBlockingConstraint, StepFunctionTypeWithTimestep, StepFunctionTypeNoTimestep
  from allennlp.common.params import Params
  from allennlp.nn.util import min_value_of_dtype
label_desc: Use image node_id 11 for calling the BeamSearchTest obj's underlying member method code with example usage: obj.test_min_steps() without return types