code: def binarizer(
      x: Union[ivy.Array, ivy.NativeArray],
      /,
      *,
      threshold: float = 0,
      out: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      """Map the values of the input tensor to either 0 or 1, element-wise, based
      on the outcome of a comparison against a threshold value.
  
      Parameters
      ----------
      x
          Data to be binarized
      threshold
          Values greater than this are
          mapped to 1, others to 0.
      out
          optional output array, for writing the result to.
          It must have a shape that the inputs broadcast to.
  
      Returns
      -------
      ret
          Binarized output data
      """
      xc = ivy.copy_array(x, out=out)
      if ivy.is_bool_dtype(xc) and ivy.current_backend_str() == "torch":
          xc = ivy.astype(xc, ivy.default_float_dtype())
      if ivy.is_complex_dtype(xc):
          xc = ivy.abs(xc)
      ret = ivy.where(xc > threshold, 1, 0)
      return ret
  
imports: from typing import Optional, Union, Tuple, List, Sequence
  from numbers import Number
  import ivy
  from ivy.func_wrapper import handle_out_argument, to_native_arrays_and_back, handle_nestable, handle_partial_mixed_function, handle_array_like_without_promotion, inputs_to_ivy_arrays, handle_array_function, infer_dtype, handle_device, handle_backend_invalid
  from ivy.utils.exceptions import handle_exceptions
label_desc: Use image node_id 20 for calling a global function with example usage: binarizer() and returns: ret