class_docstr: A simple linear decoder.
  
  Parameters
  ----------
  embed_size: The size of the value embedding dimension.
  q_size: The size of the query encoding.
  prediction_length: The length of the prediction horizon.
  num_quantiles: The number of quantiles to be predicted.
  
  
  Returns
  -------
  torch.Tensor: predictions of size [batch, prediction_length, num_quantiles]
class_docstr_tok: ["A","simple","linear","decoder",".","Parameters","--","--","--","--","--","embed_size",":","The","size","of","the","value","embedding","dimension",".","q_size",":","The","size","of","the","query","encoding",".","prediction_length",":","The","length","of","the","prediction","horizon",".","num_quantiles",":","The","number","of","quantiles","to","be","predicted",".","Returns","--","--","--","-","torch.Tensor",":","predictions","of","size","[","batch",",","prediction_length",",","num_quantiles","]"]
code: def __init__(
      self,
      num_quantiles: int,
      embed_size: int = 10,
      q_size: int = 20,
      hidden_size: int = 32,
      prediction_length: int = 1,
  ):
      super().__init__()
      self.num_quantiles = num_quantiles
      self.prediction_length = prediction_length
      self.fc1 = nn.Linear(embed_size + q_size, hidden_size)
      self.fc2 = nn.Linear(
          hidden_size, prediction_length * self.num_quantiles
      )
  
file_path: gluonts/src/gluonts/nursery/few_shot_prediction/src/meta/models/EcDc/components/decoder.py
imports: from abc import ABC, abstractmethod
  import torch
  from torch import nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new FeedForwardQuantileDecoder object from inherited base classes: Decoder with example: obj = FeedForwardQuantileDecoder(num_quantiles, embed_size, q_size, hidden_size, prediction_length)