class_docstr: Output layer using a quantile loss and projection layer to connect the
  quantile output to the network.
  
  Parameters
  ----------
  quantiles
      list of quantiles to compute loss over.
  
  quantile_weights
      weights of the quantiles.
class_docstr_tok: ["Output","layer","using","a","quantile","loss","and","projection","layer","to","connect","the","quantile","output","to","the","network",".","Parameters","--","--","--","--","--","quantiles","list","of","quantiles","to","compute","loss","over",".","quantile_weights","weights","of","the","quantiles","."]
code: def get_quantile_proj(self, **kwargs) -> nn.HybridBlock:
      return nn.Dense(units=self.num_quantiles, flatten=False)
  
imports: from typing import List, Optional
  from mxnet.gluon import nn
  from mxnet.gluon.loss import Loss
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
label_desc: Use image node_id 4 for calling the QuantileOutput obj's underlying member method code with example usage: obj.get_quantile_proj() and returns: nn