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 forward(
      self, query: torch.Tensor, value: torch.Tensor = None
  ) -> torch.Tensor:
      if value is not None:
          x = torch.cat((value, query.sequences), dim=1)
      else:
          x = query.sequences
      x = self.fc1(x)
      x = F.relu(x)
      x = self.fc2(x)
      x = x.reshape(-1, self.prediction_length, self.num_quantiles)
      return x
  
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 2 for calling the FeedForwardQuantileDecoder obj's underlying member method code with example usage: obj.forward(query, value) and returns: x