docstr: signal context function
  
  Segmenting the signal into chunks with specific context.
  input:
      x: size (B, dim, nframe)
      context: int
docstr_tok: ["signal","context","function","Segmenting","the","signal","into","chunks","with","specific","context",".","input",":","x",":","size","(","B",",","dim",",","nframe",")","context",":","int"]
code: def signal_context(self, x, context):
      """signal context function
  
      Segmenting the signal into chunks with specific context.
      input:
          x: size (B, dim, nframe)
          context: int
      """
  
      batch_size, dim, nframe = x.shape
  
      zero_pad = torch.zeros(batch_size, dim, context).type(x.type())
      pad_past = []
      pad_future = []
      for i in range(context):
          pad_past.append(
              torch.cat(
                  [zero_pad[:, :, i:], x[:, :, : -context + i]], 2
              ).unsqueeze(2)
          )
          pad_future.append(
              torch.cat(
                  [x[:, :, i + 1 :], zero_pad[:, :, : i + 1]], 2
              ).unsqueeze(2)
          )
  
      pad_past = torch.cat(pad_past, 2)  # B, D, C, L
      pad_future = torch.cat(pad_future, 2)  # B, D, C, L
      all_context = torch.cat(
          [pad_past, x.unsqueeze(2), pad_future], 2
      )  # B, D, 2*C+1, L
  
      return all_context
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from espnet2.enh.layers import dprnn
label_desc: Use image node_id 4 for calling the FaSNet_base obj's underlying member method code with example usage: obj.signal_context(x, context) and returns: all_context