code: def tile(cls, op: "DataFrameReadMLDataset"):
      count_iter = op.mldataset.for_each(lambda df: len(df))
      nsplits = [sum(shard) for shard in count_iter.shards()]
      nsplits_acc = np.cumsum(nsplits)
      out_df = op.outputs[0]
      out_chunks = []
      for shard_index in range(op.mldataset.num_shards()):
          chunk_op = op.copy().reset_key()
          # Make chunk key unique, otherwise all chunk will have same key.
          # See `DataFrameFromRecords#tile`
          chunk_op.extra_params["shard_index"] = shard_index
          shape = (nsplits[shard_index], out_df.shape[1])
          begin_index = nsplits_acc[shard_index] - nsplits[shard_index]
          end_index = nsplits_acc[shard_index]
          index = parse_index(
              pd.RangeIndex(start=begin_index, stop=end_index)
          )
          new_chunk = chunk_op.new_chunk(
              None,
              shape=shape,
              index=(shard_index, 0),
              index_value=index,
              columns_value=out_df.columns_value,
              dtypes=out_df.dtypes,
          )
          out_chunks.append(new_chunk)
      new_op = op.copy()
      nsplits = ((np.nan,) * len(out_chunks), (out_df.shape[1],))
      return new_op.new_dataframes(
          None,
          out_df.shape,
          dtypes=out_df.dtypes,
          index_value=out_df.index_value,
          columns_value=out_df.columns_value,
          chunks=out_chunks,
          nsplits=nsplits,
      )
  
imports: import functools
  import warnings
  import numpy
  import pandas
  from ...None import opcodes
  from ...core import OutputType
  from ...serialization.serializables import AnyField, BoolField, ListField, Int64Field, ReferenceField
  from ..utils import parse_index, lazy_import, tokenize
  from .core import IncrementalIndexDatasource, IncrementalIndexDataSourceMixin, HeadOptimizedDataSource
label_desc: Use image node_id 4 for calling the DataFrameReadMLDataset obj's underlying member method code with example usage: obj.tile(cls, op) and returns: new_op