class_docstr: A `CnnEncoder` is a combination of multiple convolution layers and max pooling layers.  As a
  [`Seq2VecEncoder`](./seq2vec_encoder.md), the input to this module is of shape `(batch_size, num_tokens,
  input_dim)`, and the output is of shape `(batch_size, output_dim)`.
  
  The CNN has one convolution layer for each ngram filter size. Each convolution operation gives
  out a vector of size num_filters. The number of times a convolution layer will be used
  is `num_tokens - ngram_size + 1`. The corresponding maxpooling layer aggregates all these
  outputs from the convolution layer and outputs the max.
  
  This operation is repeated for every ngram size passed, and consequently the dimensionality of
  the output after maxpooling is `len(ngram_filter_sizes) * num_filters`.  This then gets
  (optionally) projected down to a lower dimensional output, specified by `output_dim`.
  
  We then use a fully connected layer to project in back to the desired output_dim.  For more
  details, refer to "A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural
  Networks for Sentence Classification", Zhang and Wallace 2016, particularly Figure 1.
  
  Registered as a `Seq2VecEncoder` with name "cnn".
  
  # Parameters
  
  embedding_dim : `int`, required
      This is the input dimension to the encoder.  We need this because we can't do shape
      inference in pytorch, and we need to know what size filters to construct in the CNN.
  num_filters : `int`, required
      This is the output dim for each convolutional layer, which is the number of "filters"
      learned by that layer.
  ngram_filter_sizes : `Tuple[int]`, optional (default=`(2, 3, 4, 5)`)
      This specifies both the number of convolutional layers we will create and their sizes.  The
      default of `(2, 3, 4, 5)` will have four convolutional layers, corresponding to encoding
      ngrams of size 2 to 5 with some number of filters.
  conv_layer_activation : `Activation`, optional (default=`torch.nn.ReLU`)
      Activation to use after the convolution layers.
  output_dim : `Optional[int]`, optional (default=`None`)
      After doing convolutions and pooling, we'll project the collected features into a vector of
      this size.  If this value is `None`, we will just return the result of the max pooling,
      giving an output of shape `len(ngram_filter_sizes) * num_filters`.
class_docstr_tok: ["A","`","CnnEncoder","`","is","a","combination","of","multiple","convolution","layers","and","max","pooling","layers",".","As","a","[","`","Seq2VecEncoder","`","]","(",".\/seq2vec_encoder.md",")",",","the","input","to","this","module","is","of","shape","`","(","batch_size",",","num_tokens",",","input_dim",")","`",",","and","the","output","is","of","shape","`","(","batch_size",",","output_dim",")","`",".","The","CNN","has","one","convolution","layer","for","each","ngram","filter","size",".","Each","convolution","operation","gives","out","a","vector","of","size","num_filters",".","The","number","of","times","a","convolution","layer","will","be","used","is","`","num_tokens","-","ngram_size","+","1","`",".","The","corresponding","maxpooling","layer","aggregates","all","these","outputs","from","the","convolution","layer","and","outputs","the","max",".","This","operation","is","repeated","for","every","ngram","size","passed",",","and","consequently","the","dimensionality","of","the","output","after","maxpooling","is","`","len","(","ngram_filter_sizes",")","*","num_filters","`",".","This","then","gets","(","optionally",")","projected","down","to","a","lower","dimensional","output",",","specified","by","`","output_dim","`",".","We","then","use","a","fully","connected","layer","to","project","in","back","to","the","desired","output_dim",".","For","more","details",",","refer","to","``","A","Sensitivity","Analysis","of","(","and","Practitioners","\u2019","Guide","to",")","Convolutional","Neural","Networks","for","Sentence","Classification","''",",","Zhang","and","Wallace","2016",",","particularly","Figure","1",".","Registered","as","a","`","Seq2VecEncoder","`","with","name","``","cnn","''",".","#","Parameters","embedding_dim",":","`","int","`",",","required","This","is","the","input","dimension","to","the","encoder",".","We","need","this","because","we","ca","n't","do","shape","inference","in","pytorch",",","and","we","need","to","know","what","size","filters","to","construct","in","the","CNN",".","num_filters",":","`","int","`",",","required","This","is","the","output","dim","for","each","convolutional","layer",",","which","is","the","number","of","``","filters","''","learned","by","that","layer",".","ngram_filter_sizes",":","`","Tuple","[","int","]","`",",","optional","(","default=","`","(","2",",","3",",","4",",","5",")","`",")","This","specifies","both","the","number","of","convolutional","layers","we","will","create","and","their","sizes",".","The","default","of","`","(","2",",","3",",","4",",","5",")","`","will","have","four","convolutional","layers",",","corresponding","to","encoding","ngrams","of","size","2","to","5","with","some","number","of","filters",".","conv_layer_activation",":","`","Activation","`",",","optional","(","default=","`","torch.nn.ReLU","`",")","Activation","to","use","after","the","convolution","layers",".","output_dim",":","`","Optional","[","int","]","`",",","optional","(","default=","`","None","`",")","After","doing","convolutions","and","pooling",",","we","'ll","project","the","collected","features","into","a","vector","of","this","size",".","If","this","value","is","`","None","`",",","we","will","just","return","the","result","of","the","max","pooling",",","giving","an","output","of","shape","`","len","(","ngram_filter_sizes",")","*","num_filters","`","."]
code: def forward(self, tokens: torch.Tensor, mask: torch.BoolTensor):
      if mask is not None:
          tokens = tokens * mask.unsqueeze(-1)
      else:
          # If mask doesn't exist create one of shape (batch_size, num_tokens)
          mask = torch.ones(
              tokens.shape[0], tokens.shape[1], device=tokens.device
          ).bool()
  
      # Our input is expected to have shape `(batch_size, num_tokens, embedding_dim)`.  The
      # convolution layers expect input of shape `(batch_size, in_channels, sequence_length)`,
      # where the conv layer `in_channels` is our `embedding_dim`.  We thus need to transpose the
      # tensor first.
      tokens = torch.transpose(tokens, 1, 2)
      # Each convolution layer returns output of size `(batch_size, num_filters, pool_length)`,
      # where `pool_length = num_tokens - ngram_size + 1`.  We then do an activation function,
      # masking, then do max pooling over each filter for the whole input sequence.
      # Because our max pooling is simple, we just use `torch.max`.  The resultant tensor has shape
      # `(batch_size, num_conv_layers * num_filters)`, which then gets projected using the
      # projection layer, if requested.
  
      # To ensure the cnn_encoder respects masking we add a large negative value to
      # the activations of all filters that convolved over a masked token. We do this by
      # first enumerating all filters for a given convolution size (torch.arange())
      # then by comparing it to an index of the last filter that does not involve a masked
      # token (.ge()) and finally adjusting dimensions to allow for addition and multiplying
      # by a large negative value (.unsqueeze())
      filter_outputs = []
      batch_size = tokens.shape[0]
      # shape: (batch_size, 1)
      last_unmasked_tokens = mask.sum(dim=1).unsqueeze(dim=-1)
      for i in range(len(self._convolution_layers)):
          convolution_layer = getattr(self, "conv_layer_{}".format(i))
          pool_length = (
              tokens.shape[2] - convolution_layer.kernel_size[0] + 1
          )
  
          # Forward pass of the convolutions.
          # shape: (batch_size, num_filters, pool_length)
          activations = self._activation(convolution_layer(tokens))
  
          # Create activation mask.
          # shape: (batch_size, pool_length)
          indices = (
              torch.arange(pool_length, device=activations.device)
              .unsqueeze(0)
              .expand(batch_size, pool_length)
          )
          # shape: (batch_size, pool_length)
          activations_mask = indices.ge(
              last_unmasked_tokens
              - convolution_layer.kernel_size[0]
              + 1
          )
          # shape: (batch_size, num_filters, pool_length)
          activations_mask = activations_mask.unsqueeze(1).expand_as(
              activations
          )
  
          # Replace masked out values with smallest possible value of the dtype so
          # that max pooling will ignore these activations.
          # shape: (batch_size, pool_length)
          activations = activations + (
              activations_mask * min_value_of_dtype(activations.dtype)
          )
  
          # Pick out the max filters
          filter_outputs.append(activations.max(dim=2)[0])
  
      # Now we have a list of `num_conv_layers` tensors of shape `(batch_size, num_filters)`.
      # Concatenating them gives us a tensor of shape `(batch_size, num_filters * num_conv_layers)`.
      maxpool_output = (
          torch.cat(filter_outputs, dim=1)
          if len(filter_outputs) > 1
          else filter_outputs[0]
      )
  
      # Replace the maxpool activations that picked up the masks with 0s
      maxpool_output[
          maxpool_output == min_value_of_dtype(maxpool_output.dtype)
      ] = 0.0
  
      if self.projection_layer:
          result = self.projection_layer(maxpool_output)
      else:
          result = maxpool_output
      return result
  
file_path: allennlp/allennlp/modules/seq2vec_encoders/cnn_encoder.py
imports: from typing import Optional, Tuple
  import torch
  from torch.nn import Conv1d, Linear
  from allennlp.modules.seq2vec_encoders.seq2vec_encoder import Seq2VecEncoder
  from allennlp.nn import Activation
  from allennlp.nn.util import min_value_of_dtype
label_desc: Use image node_id 4 for calling the CnnEncoder obj's underlying member method code with example usage: obj.forward(tokens, mask) and returns: result