docstr: Map a given (N, T, 2) tensor to conditional interval and size means of
  the next time step.
  
  Parameters
  ----------
  data: Tensor
      tensor of shape (N, T, 2) containing the past target, with the
      first array along the last dimension containing intervals, and
      the second array containing demand sizes.
  shift: bool
      if True, the past data will be shifted forward by one time step
  state
      RNN cell state if available
  
  Returns
  -------
  conditional_means: Tensor
      tensor of shape (N, T, 2) containing conditional means for
      intervals and sizes respectively
  rnn_out_state
      output state of the RNN, returned only if the input `state` is not
      None
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code: def mu_map(
      self,
      data: Tensor,
      shift: bool = True,
      state=None,
  ) -> Tensor:
      """
      Map a given (N, T, 2) tensor to conditional interval and size means of
      the next time step.
  
      Parameters
      ----------
      data: Tensor
          tensor of shape (N, T, 2) containing the past target, with the
          first array along the last dimension containing intervals, and
          the second array containing demand sizes.
      shift: bool
          if True, the past data will be shifted forward by one time step
      state
          RNN cell state if available
  
      Returns
      -------
      conditional_means: Tensor
          tensor of shape (N, T, 2) containing conditional means for
          intervals and sizes respectively
      rnn_out_state
          output state of the RNN, returned only if the input `state` is not
          None
      """
      data = self.forwardshift(data) if shift else data
  
      if state is not None:
          rnn_out, rnn_out_state = self.rnn_cell(data, state)
      else:
          rnn_out = self.rnn_cell(data)
  
      dense_out = self.dense(rnn_out)
      model_mu = dense_out + 1e-5
  
      return model_mu if state is None else (model_mu, rnn_out_state)
  
imports: from typing import Optional, Tuple
  import mxnet
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.distribution import Distribution, DistributionOutput
  from gluonts.mx.distribution.distribution import getF
  from mxnet import gluon
label_desc: Use image node_id 5 for calling the DeepRenewalNetwork obj's underlying member method code with example usage: obj.mu_map(data, shift, state) and returns: unknown