class_docstr: If ``x >= 0`` return elementwise ``min(beta, x)``, otherwise return
  ``min(beta, alpha * x)``.
  
  Parameters
  ----------
  x: tensor<\*?, T> (Required)
  alpha: const T (Required)
  beta: const T (Required)
  
  Returns
  -------
  tensor<\*?, T>
      * A tensor of the same type and shape as ``x``.
  
  Attributes
  ----------
  T: fp16, fp32
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code: def value_inference(self):
      x = np.minimum(np.maximum(self.x.val, 0), self.beta.val)
      y = np.minimum(
          np.minimum(self.x.val, 0) * self.alpha.val, self.beta.val
      )
      return x + y
  
imports: import math
  import numpy
  from coremltools.converters.mil.mil import types
  from coremltools.converters.mil.mil.input_type import DefaultInputs, InputSpec, TensorInputType
  from coremltools.converters.mil.mil.operation import VALUE, Operation, precondition
  from coremltools.converters.mil.mil.ops.defs._op_reqs import register_op
  from .elementwise_unary import elementwise_unary
label_desc: Use image node_id 1 for calling the clamped_relu obj's underlying member method code with example usage: obj.value_inference() and returns: unknown