code: def __call__(self, y: TileableType, classes: TileableType):
      inputs = []
      if not isinstance(y, list):
          # XXX Workaround that will be removed when list of list format is
          # dropped
          self.y = y = check_array(
              y, accept_sparse=True, ensure_2d=False, dtype=None
          )
      if isinstance(y, ENTITY_TYPE):
          inputs.append(y)
      if isinstance(classes, ENTITY_TYPE):
          inputs.append(classes)
      self.sparse = self.sparse_output
      self.output_types = [OutputType.tensor]
      if len(classes) == 2:
          n_dim1 = 1
      else:
          n_dim1 = len(classes)
      return self.new_tileable(
          inputs,
          shape=(np.nan, n_dim1),
          dtype=np.dtype(int),
          order=TensorOrder.C_ORDER,
      )
  
imports: from typing import Union
  import numpy
  import scipy.sparse
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.preprocessing import label_binarize
  from sklearn.utils.sparsefuncs import min_max_axis
  from ...None import execute, fetch
  from ...None import opcodes
  from ...None import tensor
  from ...core import ENTITY_TYPE, OutputType, recursive_tile
  from ...core.context import get_context, Context
  from ...lib.sparse import SparseNDArray
  from ...serialization.serializables import AnyField, BoolField, Int32Field, StringField
  from ...tensor.core import TensorOrder
  from ...typing import TileableType
  from ..operands import LearnOperand, LearnOperandMixin
  from ..utils import column_or_1d
  from ..utils._encode import _unique, _encode
  from ..utils.multiclass import unique_labels, type_of_target
  from ..utils.validation import _num_samples, check_is_fitted, check_array
label_desc: Use image node_id 1 for calling the LabelBinarize obj's underlying member method code with example usage: obj.__call__(y, classes) and returns: self