class_docstr: represent tensor from DataFrame
code: def tile(cls, op: "TensorFromDataFrame"):
      output = op.outputs[0]
  
      out_chunks = []
      for c in op.input.chunks:
          shape = (
              (c.shape[0], output.shape[1])
              if op._extract_multi_index
              else c.shape
          )
          index = (
              (c.index[0], 0) if op._extract_multi_index else c.index
          )
          out_chunk = (
              op.copy()
              .reset_key()
              .new_chunk(
                  [c], shape=shape, index=index, order=output.order
              )
          )
          out_chunks.append(out_chunk)
  
      new_op = op.copy()
      nsplits = (
          (op.input.nsplits[0], (output.shape[1],))
          if op._extract_multi_index
          else op.input.nsplits
      )
      return new_op.new_tensors(
          op.inputs,
          output.shape,
          order=output.order,
          chunks=out_chunks,
          nsplits=nsplits,
      )
  
file_path: mars/mars/tensor/datasource/from_dataframe.py
imports: from ...None import opcodes
  from ...serialization.serializables import BoolField, KeyField
  from ..utils import to_numpy
  from ..core import TensorOrder
  from .core import TensorHasInput
label_desc: Use image node_id 3 for calling the TensorFromDataFrame obj's underlying member method code with example usage: obj.tile(cls, op) and returns: new_op