class_docstr: A simple module that computes the projection of an
  embedding vector onto the specified number of dimensions
  
  Arguments
  ---------
  emb_dim: int
      the original embedding dimensionality
  
  proj_dim: int
      the dimensionality of the target projection
      space
  
  Example
  -------
  >>> mod_emb_proj = EmbeddingProjection(emb_dim=16, proj_dim=64)
  >>> emb = torch.randn(4, 16)
  >>> emb_proj = mod_emb_proj(emb)
  >>> emb_proj.shape
  torch.Size([4, 64])
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docstr: Computes the forward pass
  
  Arguments
  ---------
  emb: torch.Tensor
      the original embedding tensor
  
  Returns
  -------
  result: torch.Tensor
      the target embedding space
docstr_tok: ["Computes","the","forward","pass","Arguments","--","--","--","--","-","emb",":","torch.Tensor","the","original","embedding","tensor","Returns","--","--","--","-","result",":","torch.Tensor","the","target","embedding","space"]
code: def forward(self, emb):
      """Computes the forward pass
  
      Arguments
      ---------
      emb: torch.Tensor
          the original embedding tensor
  
      Returns
      -------
      result: torch.Tensor
          the target embedding space
      """
      x = self.input(emb)
      x = self.act(x)
      x = self.output(x)
      return x
  
imports: from abc import abstractmethod
  from speechbrain.utils.data_utils import pad_divisible
  from .autoencoders import NormalizingAutoencoder
  import math
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the EmbeddingProjection obj's underlying member method code with example usage: obj.forward(emb) and returns: x