docstr: Maps raw tensors to valid arguments for constructing a Weibull
  distribution.
  
  Parameters
  ----------
  F
      MXNet backend.
  rate
      Rate (inverse scale) parameter of the Weibull distribution.
      Shape `(*batch_shape, 1)`
  shape
      Shape parameter of the Weibull distribution.
      Shape `(*batch_shape, 1)`
  
  Returns
  -------
  Tuple[Tensor, Tensor]
      Two squeezed tensors of shape `(*batch_shape)`. Both tensors are
      strictly positive.
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code: def domain_map(cls, F, rate, shape):
      r"""
      Maps raw tensors to valid arguments for constructing a Weibull
      distribution.
  
      Parameters
      ----------
      F
          MXNet backend.
      rate
          Rate (inverse scale) parameter of the Weibull distribution.
          Shape `(*batch_shape, 1)`
      shape
          Shape parameter of the Weibull distribution.
          Shape `(*batch_shape, 1)`
  
      Returns
      -------
      Tuple[Tensor, Tensor]
          Two squeezed tensors of shape `(*batch_shape)`. Both tensors are
          strictly positive.
      """
      rate = F.Activation(rate, "softrelu")
      shape = F.Activation(shape, "softrelu")
      return rate.squeeze(axis=-1), shape.squeeze(axis=-1)
  
imports: from typing import Dict, Optional, Tuple
  import numpy
  from mxnet import autograd, nd
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.distribution.distribution import getF
  from .base import TPPDistribution, TPPDistributionOutput
label_desc: Use image node_id 1 for calling the WeibullOutput obj's underlying member method code with example usage: obj.domain_map(cls, F, rate, shape) and returns: rate,  shape