class_docstr: Return ``alpha_i * log( 1 + e^( beta_i * x_i ) )``, where ``i = 1 ... C``.
  
  Parameters
  ----------
  x: tensor<[b, C, n, m], T> (Required)
  alpha: const tensor<[C], T> (Required)
  beta: const tensor<[C], T> (Required)
  
  Returns
  -------
  tensor<[b, C, n, m], T>
      * A tensor of the same shape as ``x``.
  
  Attributes
  ----------
  T: fp16, fp32
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code: def value_inference(self):
      alpha_br = np.copy(self.alpha.val)
      beta_br = np.copy(self.beta.val)
      # Expends alpha and beta on all dims besides the channel (2nd) dim.
      for i in range(len(self.x.val.shape)):
          if i != 1:
              alpha_br = np.expand_dims(alpha_br, i)
              beta_br = np.expand_dims(beta_br, i)
      return alpha_br * np.log(1 + np.exp(self.x.val * beta_br))
  
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 softplus_parametric obj's underlying member method code with example usage: obj.value_inference() and returns: unknown