code: def forward(self, input_tensor):
      """
      Apply dropout to input tensor.
  
      # Parameters
  
      input_tensor : `torch.FloatTensor`
          A tensor of shape `(batch_size, num_timesteps, embedding_dim)`
  
      # Returns
  
      output : `torch.FloatTensor`
          A tensor of shape `(batch_size, num_timesteps, embedding_dim)` with dropout applied.
      """
      ones = input_tensor.data.new_ones(
          input_tensor.shape[0], input_tensor.shape[-1]
      )
      dropout_mask = torch.nn.functional.dropout(
          ones, self.p, self.training, inplace=False
      )
      if self.inplace:
          input_tensor *= dropout_mask.unsqueeze(1)
          return None
      else:
          return dropout_mask.unsqueeze(1) * input_tensor
  
imports: import torch
label_desc: Use image node_id 1 for calling the InputVariationalDropout obj's underlying member method code with example usage: obj.forward(input_tensor) and returns: None, unknown