class_docstr: Chomp1d
  
  Receives `x` input of dim [N,C,T], and trims it so that only
  'time available' information is used.
  Used by one dimensional causal convolutions `CausalConv1d`.
  
  **Parameters:**<br>
  `horizon`: int, length of outsample values to skip.
class_docstr_tok: ["Chomp1d","Receives","`","x","`","input","of","dim","[","N",",","C",",","T","]",",","and","trims","it","so","that","only","'time","available","'","information","is","used",".","Used","by","one","dimensional","causal","convolutions","`","CausalConv1d","`",".","*","*","Parameters",":","*","*","<","br",">","`","horizon","`",":","int",",","length","of","outsample","values","to","skip","."]
code: def forward(self, x):
      return x[:, :, : -self.horizon].contiguous()
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the Chomp1d obj's underlying member method code with example usage: obj.forward(x) and returns: x