Compute the conditional log likelihood of a sequence of tags given emission scores. Args: emissions (`~torch.Tensor`): Emission sc

input: [N, C] target: [N, ]

:param z: shape (b, latent_dim)

r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`): Labels f

:class:`LongformerSelfAttention` expects `len(hidden_states)` to be multiple of `attention_window`. Padding to `attention_window` hap

r""" Returns: Examples:: >>> import torch >>> from transformers import LongformerModel, LongformerTokenizer

r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Labels for computing the masked l

r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`): Labels for computing the sequence classification/

r""" start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`): Labels for position (index) of the start

r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Labels for computing the token cl

r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`): Labels for computing the multiple choice classifi

r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformer

Call the module Args: x (`torch.tensor`): The input tensor to apply dropout

Call the module Args: hidden_states (`torch.FloatTensor`): Input states to the module usually the output

r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language mo

r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression

r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classificati

r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the la

r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification los

Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).

r""" Returns: Example:: >>> from transformers import T5Tokenizer, T5Model >>> tokenizer = T5Tokenizer.from_

r""" Returns: Example:: >>> from transformers import T5Tokenizer, T5EncoderModel >>> tokenizer = T5Tokenizer