(self, data)
return query_logits
Embeds all (training and testing) images of the episode.

    Args:
      data: A `meta_dataset.providers.Episode` containing the data for the
        episode.

    Returns:
      The predictions for the query set within the episode.
Embeds all (training and testing) images of the episode.
Embeds all ( training and testing ) images of the episode .
def forward_pass(self, data):
    """Embeds all (training and testing) images of the episode.

    Args:
      data: A `meta_dataset.providers.Episode` containing the data for the
        episode.

    Returns:
      The predictions for the query set within the episode.
    """
    # Compute the support set's mean and var and use these as the moments for
    # batch norm on the query set.
    support_embeddings_dict = self.embedding_fn(
        data.support_images,
        self.is_training,
        keep_spatial_dims=self.keep_spatial_dims)
    support_embeddings = support_embeddings_dict['embeddings']
    support_set_moments = None
    if not self.transductive_batch_norm:
      support_set_moments = support_embeddings_dict['moments']
    query_embeddings_dict = self.embedding_fn(
        data.query_images,
        self.is_training,
        moments=support_set_moments,
        keep_spatial_dims=self.keep_spatial_dims,
        backprop_through_moments=self.backprop_through_moments)
    query_embeddings = query_embeddings_dict['embeddings']

    query_logits = self.compute_logits_for_episode(
        support_embeddings,
        query_embeddings,
        data,
    )

    return query_logits
def forward_pass ( self , data ) : # Compute the support set's mean and var and use these as the moments for # batch norm on the query set. support_embeddings_dict = self . embedding_fn ( data . support_images , self . is_training , keep_spatial_dims = self . keep_spatial_dims ) support_embeddings = support_embeddings_dict [ 'embeddings' ] support_set_moments = None if not self . transductive_batch_norm : support_set_moments = support_embeddings_dict [ 'moments' ] query_embeddings_dict = self . embedding_fn ( data . query_images , self . is_training , moments = support_set_moments , keep_spatial_dims = self . keep_spatial_dims , backprop_through_moments = self . backprop_through_moments ) query_embeddings = query_embeddings_dict [ 'embeddings' ] query_logits = self . compute_logits_for_episode ( support_embeddings , query_embeddings , data , ) return query_logits