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.colocate_with(self._handle):
        with ops.op_scope(
            [self._handle, value, lengths], name, "TensorArraySplit"):
          lengths_64 = math_ops.to_int64(lengths)
        flow_out = gen_data_flow_ops._tensor_array_split(
            handle=self._handle, value=value, lengths=lengths_64,
            flow_in=self._flow, name=name)
        ta = TensorArray(dtype=self._dtype, handle=self._handle)
        ta._flow = flow_out
        ta._infer_shape = self._infer_shape
        ta._elem_shape = self._elem_shape
        if ta._infer_shape:
          val_shape = flow_out.op.inputs[1].get_shape()
          clengths = tensor_util.constant_value(flow_out.op.inputs[2])
          elem_shape = tensor_shape.unknown_shape()
          if val_shape.dims is not None:
            if clengths is not None and clengths.max() == clengths.min():
              elem_shape = tensor_shape.TensorShape(
                  [clengths[0]] + val_shape.dims[1:])
          if ta._elem_shape:
            if not elem_shape == ta._elem_shape[0]:
              raise ValueError(
                  "Inconsistent shapes: saw %s but expected %s "
                  "(and infer_shape=True)" % (elem_shape, ta._elem_shape[0]))
          else:
            ta._elem_shape.append(elem_shape)
        return ta
function_tokens: def split ( self , value , lengths , name = None ) : with ops . colocate_with ( self . _handle ) : with ops . op_scope ( [ self . _handle , value , lengths ] , name , "TensorArraySplit" ) : lengths_64 = math_ops . to_int64 ( lengths ) flow_out = gen_data_flow_ops . _tensor_array_split ( handle = self . _handle , value = value , lengths = lengths_64 , flow_in = self . _flow , name = name ) ta = TensorArray ( dtype = self . _dtype , handle = self . _handle ) ta . _flow = flow_out ta . _infer_shape = self . _infer_shape ta . _elem_shape = self . _elem_shape if ta . _infer_shape : val_shape = flow_out . op . inputs [ 1 ] . get_shape ( ) clengths = tensor_util . constant_value ( flow_out . op . inputs [ 2 ] ) elem_shape = tensor_shape . unknown_shape ( ) if val_shape . dims is not None : if clengths is not None and clengths . max ( ) == clengths . min ( ) : elem_shape = tensor_shape . TensorShape ( [ clengths [ 0 ] ] + val_shape . dims [ 1 : ] ) if ta . _elem_shape : if not elem_shape == ta . _elem_shape [ 0 ] : raise ValueError ( "Inconsistent shapes: saw %s but expected %s " "(and infer_shape=True)" % ( elem_shape , ta . _elem_shape [ 0 ] ) ) else : ta . _elem_shape . append ( elem_shape ) return ta