code: def static_call(x, negative_slope=0.0, max_value=None, threshold=0.0):
      x = backend.convert_to_tensor(x)
      if negative_slope != 0.0:
          if max_value is None and threshold == 0:
              return backend.nn.leaky_relu(
                  x, negative_slope=negative_slope
              )
  
          if threshold != 0:
              negative_part = backend.nn.relu(-x + threshold)
          else:
              negative_part = backend.nn.relu(-x)
  
      clip_max = max_value is not None
      if threshold != 0:
          # computes x for x > threshold else 0
          threshold = ops.cast(threshold, dtype=x.dtype)
          x = x * backend.cast(
              backend.numpy.greater(x, threshold), dtype=x.dtype
          )
      elif max_value == 6:
          # if no threshold, then can use nn.relu6 native op for performance
          x = backend.nn.relu6(x)
          clip_max = False
      else:
          x = backend.nn.relu(x)
  
      if clip_max:
          min_value = ops.cast(0.0, dtype=x.dtype)
          max_value = ops.cast(max_value, dtype=x.dtype)
          x = backend.numpy.clip(x, min_value, max_value)
  
      if negative_slope != 0.0:
          x -= negative_slope * negative_part
      return x
  
imports: from keras import backend
  from keras import ops
  from keras.api_export import keras_export
label_desc: Use image node_id 4 for calling the ReLU obj's underlying member method code with example usage: obj.static_call(x, negative_slope, max_value, threshold) and returns: x, backend