class_docstr: Diagnostic and specification tests and plots for Count model
  
  status: experimental
  
  Parameters
  ----------
  results : Results instance of a count model.
  y_max : int
      Largest count to include when computing predicted probabilities for
      counts. Default is the largest observed count.
docstr: Plot observed versus predicted frequencies for entire sample.
          
docstr_tok: ["Plot","observed","versus","predicted","frequencies","for","entire","sample","."]
code: def plot_probs(self, label="predicted", upp_xlim=None, fig=None):
      """Plot observed versus predicted frequencies for entire sample."""
      probs_predicted = self.probs_predicted.sum(0)
      k_probs = len(probs_predicted)
      freq = np.bincount(
          self.results.model.endog.astype(int), minlength=k_probs
      )[:k_probs]
      fig = plot_probs(
          freq, probs_predicted, label=label, upp_xlim=upp_xlim, fig=fig
      )
      return fig
  
file_path: statsmodels/statsmodels/discrete/diagnostic.py
imports: import warnings
  import numpy
  from statsmodels.tools.decorators import cache_readonly
  from statsmodels.stats.diagnostic_gen import test_chisquare_binning
  from statsmodels.discrete._diagnostics_count import test_poisson_dispersion, test_poisson_zeroinflation_jh, test_poisson_zeroinflation_broek, test_poisson_zeros, test_chisquare_prob, plot_probs
label_desc: Use image node_id 4 for calling the CountDiagnostic obj's underlying member method code with example usage: obj.plot_probs(label, upp_xlim, fig) and returns: fig