parameters: (self, value, lengths, name=None)
docstring: Split the values of a `Tensor` into the TensorArray.
  
      Args:
        value: (N+1)-D.  Tensor of type `dtype`.  The Tensor to split.
        lengths: 1-D.  int32 vector with the lengths to use when splitting
          `value` along its first dimension.
        name: A name for the operation (optional).
  
      Returns:
        A new TensorArray object with flow that ensures the split occurs.
        Use this object all for subsequent operations.
  
      Raises:
        ValueError: if the shape inference fails.
docstring_summary: Split the values of a `Tensor` into the TensorArray.
docstring_tokens: Split the values of a Tensor into the TensorArray .
function: def split(self, value, lengths, name=None):
      """Split the values of a `Tensor` into the TensorArray.
  
      Args:
        value: (N+1)-D.  Tensor of type `dtype`.  The Tensor to split.
        lengths: 1-D.  int32 vector with the lengths to use when splitting
          `value` along its first dimension.
        name: A name for the operation (optional).
  
      Returns:
        A new TensorArray object with flow that ensures the split occurs.
        Use this object all for subsequent operations.
  
      Raises:
        ValueError: if the shape inference fails.
      """
      with ops.name_scope(name, "TensorArraySplit",
                          [self._handle, value, lengths]):
        value = ops.convert_to_tensor(value, name="value")
        with self._maybe_colocate_with(value):
          lengths_64 = math_ops.to_int64(lengths)
          flow_out = gen_data_flow_ops._tensor_array_split_v3(
              handle=self._handle,
              value=value,
              lengths=lengths_64,
              flow_in=self._flow,
              name=name)
        ta = TensorArray(
            dtype=self._dtype, handle=self._handle, flow=flow_out,
            colocate_with_first_write_call=self._colocate_with_first_write_call)
        ta._infer_shape = self._infer_shape
        ta._element_shape = self._element_shape
        ta._colocate_with = self._colocate_with
        if ta._infer_shape:
          val_shape = flow_out.op.inputs[1].get_shape()
          clengths = tensor_util.constant_value(flow_out.op.inputs[2])
          element_shape = tensor_shape.unknown_shape()
          if val_shape.dims is not None:
            if clengths is not None and clengths.max() == clengths.min():
              element_shape = tensor_shape.TensorShape([clengths[0]] +
                                                       val_shape.dims[1:])
          ta._merge_element_shape(element_shape)
        return ta
function_tokens: def split ( self , value , lengths , name = None ) : with ops . name_scope ( name , "TensorArraySplit" , [ self . _handle , value , lengths ] ) : value = ops . convert_to_tensor ( value , name = "value" ) with self . _maybe_colocate_with ( value ) : lengths_64 = math_ops . to_int64 ( lengths ) flow_out = gen_data_flow_ops . _tensor_array_split_v3 ( handle = self . _handle , value = value , lengths = lengths_64 , flow_in = self . _flow , name = name ) ta = TensorArray ( dtype = self . _dtype , handle = self . _handle , flow = flow_out , colocate_with_first_write_call = self . _colocate_with_first_write_call ) ta . _infer_shape = self . _infer_shape ta . _element_shape = self . _element_shape ta . _colocate_with = self . _colocate_with if ta . _infer_shape : val_shape = flow_out . op . inputs [ 1 ] . get_shape ( ) clengths = tensor_util . constant_value ( flow_out . op . inputs [ 2 ] ) element_shape = tensor_shape . unknown_shape ( ) if val_shape . dims is not None : if clengths is not None and clengths . max ( ) == clengths . min ( ) : element_shape = tensor_shape . TensorShape ( [ clengths [ 0 ] ] + val_shape . dims [ 1 : ] ) ta . _merge_element_shape ( element_shape ) return ta