code: def write_megam_file(
      train_toks, encoding, stream, bernoulli=True, explicit=True
  ):
      """
      Generate an input file for ``megam`` based on the given corpus of
      classified tokens.
  
      :type train_toks: list(tuple(dict, str))
      :param train_toks: Training data, represented as a list of
          pairs, the first member of which is a feature dictionary,
          and the second of which is a classification label.
  
      :type encoding: MaxentFeatureEncodingI
      :param encoding: A feature encoding, used to convert featuresets
          into feature vectors. May optionally implement a cost() method
          in order to assign different costs to different class predictions.
  
      :type stream: stream
      :param stream: The stream to which the megam input file should be
          written.
  
      :param bernoulli: If true, then use the 'bernoulli' format.  I.e.,
          all joint features have binary values, and are listed iff they
          are true.  Otherwise, list feature values explicitly.  If
          ``bernoulli=False``, then you must call ``megam`` with the
          ``-fvals`` option.
  
      :param explicit: If true, then use the 'explicit' format.  I.e.,
          list the features that would fire for any of the possible
          labels, for each token.  If ``explicit=True``, then you must
          call ``megam`` with the ``-explicit`` option.
      """
      # Look up the set of labels.
      labels = encoding.labels()
      labelnum = {label: i for (i, label) in enumerate(labels)}
  
      # Write the file, which contains one line per instance.
      for featureset, label in train_toks:
          # First, the instance number (or, in the weighted multiclass case, the cost of each label).
          if hasattr(encoding, "cost"):
              stream.write(
                  ":".join(
                      str(encoding.cost(featureset, label, l))
                      for l in labels
                  )
              )
          else:
              stream.write("%d" % labelnum[label])
  
          # For implicit file formats, just list the features that fire
          # for this instance's actual label.
          if not explicit:
              _write_megam_features(
                  encoding.encode(featureset, label), stream, bernoulli
              )
  
          # For explicit formats, list the features that would fire for
          # any of the possible labels.
          else:
              for l in labels:
                  stream.write(" #")
                  _write_megam_features(
                      encoding.encode(featureset, l), stream, bernoulli
                  )
  
          # End of the instance.
          stream.write("\n")
  
imports: import subprocess
  from nltk.internals import find_binary
label_desc: Use image node_id 2 for calling a global function with example usage: write_megam_file(train_toks, encoding, stream, bernoulli, explicit) without return types