code: def tovw(x, y=None, sample_weight=None, convert_labels=False):
      """Convert array or sparse matrix to Vowpal Wabbit format
  
      Args:
  
          x : {array-like, sparse matrix}, shape (n_samples, n_features)
              Training vector, where n_samples is the number of samples and
              n_features is the number of features.
          y : {array-like}, shape (n_samples,), optional
              Target vector relative to X.
          sample_weight : {array-like}, shape (n_samples,), optional
                          sample weight vector relative to X.
          convert_labels : {bool} convert labels of the form [0,1] to [-1,1]
  
      Returns:
          {array-like}, shape (n_samples, 1)
              Training vectors in VW string format
  
      Examples:
          >>> import pandas as pd
          >>> from sklearn.feature_extraction.text import HashingVectorizer
          >>> from vowpalwabbit.sklearn import tovw
          >>> X = pd.Series(['cat', 'dog', 'cat', 'cat'], name='catdog')
          >>> y = pd.Series([-1, 1, -1, -1], name='label')
          >>> hv = HashingVectorizer()
          >>> hashed = hv.fit_transform(X)
          >>> tovw(x=hashed, y=y)
          ['-1 1 | 300839:1', '1 1 | 980517:-1', '-1 1 | 300839:1', '-1 1 | 300839:1']
      """
  
      use_truth = y is not None
      use_weight = sample_weight is not None
  
      if use_truth:
          x, y = check_X_y(x, y, accept_sparse=True)
      else:
          x = check_array(x, accept_sparse=True)
  
      if use_weight:
          sample_weight = check_array(
              sample_weight,
              accept_sparse=False,
              ensure_2d=False,
              dtype=int,
              order="C",
          )
          if sample_weight.ndim != 1:
              raise ValueError(
                  "Sample weights must be 1D array or scalar"
              )
          if sample_weight.shape != (x.shape[0],):
              raise ValueError(
                  "Sample weight shape == {}, expected {}".format(
                      sample_weight.shape, (x.shape[0],)
                  )
              )
      else:
          sample_weight = np.ones(x.shape[0], dtype=int)
  
      # convert labels of the form [0,1] to [-1,1]
      if convert_labels:
          y = np.where(y < 1, -1, 1)
  
      rows, cols = x.shape
  
      # check for invalid characters if array has string values
      if x.dtype.char == "S":
          for row in rows:
              for col in cols:
                  x[row, col] = INVALID_CHARS.sub(".", x[row, col])
      elif x.dtype.kind == "u":
          raise TypeError(
              "tovw does not support unsigned integers. Please convert to signed integers. See issue: https://github.com/VowpalWabbit/vowpal_wabbit/issues/4609"
          )
  
      # convert input to svmlight format
      s = io.BytesIO()
      dump_svmlight_file(x, np.zeros(rows), s)
  
      # parse entries to construct VW format
      rows = s.getvalue().decode("ascii").split("\n")[:-1]
      out = []
      for idx, row in enumerate(rows):
          truth = y[idx] if use_truth else 1
          weight = sample_weight[idx]
          features = row.split("0 ", 1)[1]
          # only using a single namespace and no tags
          out.append(
              (
                  "{y} {w} |{ns} {x}".format(
                      y=truth, w=weight, ns=DEFAULT_NS, x=features
                  )
              )
          )
  
      s.close()
  
      return out
  
imports: import io
  import os
  import re
  from tempfile import NamedTemporaryFile
  from typing import Dict
  import numpy
  from scipy.sparse import csr_matrix
  from scipy.special import logit
  from sklearn.exceptions import NotFittedError
  from sklearn.base import BaseEstimator, RegressorMixin
  from sklearn.utils.extmath import log_logistic
  from sklearn.linear_model import LogisticRegression
  from sklearn.datasets import dump_svmlight_file
  from sklearn.utils import check_array, check_X_y, shuffle
  from vowpalwabbit import Workspace
label_desc: Use image node_id 1 for calling a global function with example usage: tovw(x, y, sample_weight, convert_labels) and returns: out