code: def split_and_load(data, ctx_list, batch_axis=0, even_split=True):
      
      if not isinstance(data, ndarray.NDArray):
          data = ndarray.array(data, ctx=ctx_list[0])
      if len(ctx_list) == 1:
          return [data.as_in_context(ctx_list[0])]
  
      slices = split_data(data, len(ctx_list), batch_axis, even_split)
      return [i.as_in_context(ctx) for i, ctx in zip(slices, ctx_list)]
docs: Splits an NDArray into `len(ctx_list)` slices along `batch_axis` and loads
      each slice to one context in `ctx_list`.
  
      Parameters
      ----------
      data : NDArray
          A batch of data.
      ctx_list : list of Context
          A list of Contexts.
      batch_axis : int, default 0
          The axis along which to slice.
      even_split : bool, default True
          Whether to force all slices to have the same number of elements.
  
      Returns
      -------
      list of NDArray
          Each corresponds to a context in `ctx_list`.