code: def forward(self, input_tensor):
      # pylint: disable=arguments-differ
      """
      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: from __future__ import absolute_import
  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