code: def tile(cls, op: "TensorBinCount"):
      from ...dataframe.operands import DataFrameShuffleProxy
      from ...dataframe.utils import parse_index
  
      if has_unknown_shape(*op.inputs):
          yield
  
      ctx = get_context()
      a = op.inputs[0]
      out = op.outputs[0]
  
      if op.weights is not None and a.shape != op.weights.shape:
          raise ValueError(
              "The weights and list don't have the same length."
          )
  
      input_max = yield from recursive_tile(a.max())
      yield input_max.chunks + [
          c for inp in op.inputs for c in inp.chunks
      ]
      [max_val] = ctx.get_chunks_result([input_max.chunks[0].key])
      tileable_right_bound = max(op.minlength, int(max_val) + 1)
  
      chunk_count = max(
          1, ceildiv(tileable_right_bound, op.chunk_size_limit)
      )
  
      if (
          len(op.inputs[0].chunks) == 1
          and (op.weights is None or len(op.weights.chunks) == 1)
          and chunk_count == 1
      ):
          return cls._tile_single(op)
  
      if op.weights is not None:
          weights = yield from recursive_tile(
              op.weights.rechunk(a.nsplits)
          )
          weights_chunks = weights.chunks
      else:
          weights_chunks = itertools.repeat(None)
  
      map_chunks = []
      for a_chunk, weights_chunk in zip(a.chunks, weights_chunks):
          new_op = op.copy().reset_key()
          new_op.chunk_count = chunk_count
          new_op.tileable_right_bound = tileable_right_bound
          new_op.stage = OperandStage.map
          new_op._output_types = [OutputType.series]
  
          inputs = [a_chunk]
          if weights_chunk is not None:
              inputs.append(weights_chunk)
          map_chunks.append(
              new_op.new_chunk(
                  inputs,
                  dtype=out.dtype,
                  shape=(np.nan,),
                  index=a_chunk.index,
                  index_value=parse_index(
                      pd.Index([0], dtype=np.int64), a_chunk.key
                  ),
              )
          )
  
      shuffle_op = DataFrameShuffleProxy(
          output_types=[OutputType.tensor]
      ).new_chunk(map_chunks, dtype=out.dtype, shape=())
  
      reduce_chunks = []
      reduce_nsplits = []
      left_offset = 0
      for chunk_idx in range(chunk_count):
          right_offset = min(
              tileable_right_bound, left_offset + op.chunk_size_limit
          )
  
          new_op = op.copy().reset_key()
          new_op.stage = OperandStage.reduce
          new_op.reducer_ordinal = chunk_idx
          new_op.n_reducers = chunk_count
          new_op.chunk_count = chunk_count
          new_op.tileable_right_bound = tileable_right_bound
  
          reduce_chunks.append(
              new_op.new_chunk(
                  [shuffle_op],
                  dtype=out.dtype,
                  shape=(right_offset - left_offset,),
                  index=(chunk_idx,),
              )
          )
          reduce_nsplits.append(right_offset - left_offset)
          left_offset = right_offset
  
      new_op = op.copy().reset_key()
      params = out.params.copy()
      params["shape"] = (tileable_right_bound,)
      return new_op.new_tileables(
          op.inputs,
          chunks=reduce_chunks,
          nsplits=(tuple(reduce_nsplits),),
          **params,
      )
  
imports: import itertools
  import functools
  from typing import Optional
  import numpy
  import pandas
  from ...None import opcodes, options, get_context
  from ...core import recursive_tile, OutputType
  from ...core.operand import OperandStage
  from ...serialization.serializables import Int64Field, ReferenceField
  from ...utils import ceildiv, has_unknown_shape
  from ..datasource import tensor
  from ..operands import TensorOperandMixin, TensorMapReduceOperand
label_desc: Use image node_id 4 for calling the TensorBinCount obj's underlying member method code with example usage: obj.tile(cls, op) and returns: new_op, cls