code: def copysign(
      x1: Union[tf.Tensor, tf.Variable, Number],
      x2: Union[tf.Tensor, tf.Variable, Number],
      /,
      *,
      out: Optional[tf.Tensor] = None,
  ) -> Union[tf.Tensor, tf.Variable]:
      x1, x2 = promote_types_of_inputs(x1, x2)
      # Cast our inputs to float to match numpy behaviour
      if not ivy.is_float_dtype(x1):
          x1 = tf.cast(x1, ivy.default_float_dtype(as_native=True))
          x2 = tf.cast(x2, ivy.default_float_dtype(as_native=True))
      # Replace any zero values with 1/the value, since tf.math.sign always
      # returns 0 for positive or negative zero
      signable_x2 = tf.where(tf.equal(x2, 0), tf.math.divide(1, x2), x2)
      signs = tf.math.sign(signable_x2)
      return tf.math.multiply(tf.math.abs(x1), signs)
  
imports: import operator
  from typing import Union, Optional, Tuple, List, Sequence
  from numbers import Number
  import tensorflow
  from tensorflow.python.ops.numpy_ops import np_math_ops
  import ivy
  from ivy import promote_types_of_inputs
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  from ..None import backend_version
label_desc: Use image node_id 7 for calling a global function with example usage: copysign() and returns: tf