code: def tile(cls, op):
      from ..indexing.slice import TensorSlice
  
      inputs = op.inputs
      output = op.outputs[0]
      axis = op.axis
  
      c = itertools.count(inputs[0].ndim)
      tensor_axes = [
          (t, tuple(i if i != axis else next(c) for i in range(t.ndim)))
          for t in inputs
      ]
      inputs = yield from unify_chunks(*tensor_axes)
  
      out_chunk_shape = [
          0 if i == axis else inputs[0].chunk_shape[i]
          for i in range(inputs[0].ndim)
      ]
      out_chunk_shape[axis] = sum(t.chunk_shape[axis] for t in inputs)
      out_nsplits = [
          None if i == axis else inputs[0].nsplits[i]
          for i in range(inputs[0].ndim)
      ]
      out_nsplits[axis] = tuple(
          itertools.chain(*[t.nsplits[axis] for t in inputs])
      )
  
      out_chunks = []
      axis_cum_chunk_shape = np.cumsum(
          [t.chunk_shape[axis] for t in inputs]
      )
      for out_idx in itertools.product(
          *[range(s) for s in out_chunk_shape]
      ):
          axis_index = np.searchsorted(
              axis_cum_chunk_shape, out_idx[axis], side="right"
          )
          t = inputs[axis_index]
          axis_inner_index = out_idx[axis] - (
              0
              if axis_index < 1
              else axis_cum_chunk_shape[axis_index - 1]
          )
          idx = (
              out_idx[:axis] + (axis_inner_index,) + out_idx[axis + 1 :]
          )
          in_chunk = t.cix[idx]
          if idx == out_idx:
              # if index is the same, just use the input chunk
              out_chunks.append(in_chunk)
          else:
              chunk_op = TensorSlice(
                  slices=[slice(None) for _ in range(in_chunk.ndim)],
                  dtype=in_chunk.dtype,
                  sparse=in_chunk.op.sparse,
              )
              out_chunk = chunk_op.new_chunk(
                  [in_chunk],
                  shape=in_chunk.shape,
                  index=out_idx,
                  order=output.order,
              )
  
              out_chunks.append(out_chunk)
  
      new_op = op.copy()
      return new_op.new_tensors(
          op.inputs,
          output.shape,
          order=output.order,
          nsplits=out_nsplits,
          chunks=out_chunks,
      )
  
imports: import itertools
  import operator
  import tempfile
  from collections.abc import Iterable
  import numpy
  from ...None import opcodes
  from ...serialization.serializables import AnyField, BoolField, StringField, TupleField, SliceField
  from ..array_utils import device, as_same_device
  from ..utils import validate_axis, unify_chunks
  from ..datasource import tensor
  from ..operands import TensorOperand, TensorOperandMixin
  from ..indexing.slice import TensorSlice
label_desc: Use image node_id 9 for calling the TensorConcatenate obj's underlying member method code with example usage: obj.tile(cls, op) and returns: new_op