class_docstr: Tensorflow printer which handles vectorized piecewise functions,
  logical operators, max/min, and relational operators.
class_docstr_tok: ["Tensorflow","printer","which","handles","vectorized","piecewise","functions",",","logical","operators",",","max\/min",",","and","relational","operators","."]
code: def _print_Derivative(self, expr):
      variables = expr.variables
      if any(isinstance(i, Iterable) for i in variables):
          raise NotImplementedError(
              "derivation by multiple variables is not supported"
          )
  
      def unfold(expr, args):
          if not args:
              return self._print(expr)
          return "%s(%s, %s)[0]" % (
              self._module_format("tensorflow.gradients"),
              unfold(expr, args[:-1]),
              self._print(args[-1]),
          )
  
      return unfold(expr.expr, variables)
  
imports: from sympy.external.importtools import version_tuple
  from collections.abc import Iterable
  from sympy.core.mul import Mul
  from sympy.core.singleton import S
  from sympy.codegen.cfunctions import Sqrt
  from sympy.external import import_module
  from sympy.printing.precedence import PRECEDENCE
  from sympy.printing.pycode import AbstractPythonCodePrinter, ArrayPrinter
  import sympy
label_desc: Use image node_id 5 for calling the TensorflowPrinter obj's underlying member method code with example usage: obj._print_Derivative(expr) and returns: unfold, unknown, self