r""" A cross attention layer. Parameters: query_dim (`int`): The number of channels in the query. cross_attention

r""" GELU activation function with tanh approximation support with `approximate="tanh"`. Parameters: dim_in (`int`): The number of ch

r""" The approximate form of Gaussian Error Linear Unit (GELU). For more details, see section 2: https://arxiv.org/abs/1606.08415. Parame

r""" Default processor for performing attention-related computations.

r""" Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).

r""" A basic Transformer block. Parameters: dim (`int`): The number of channels in the input and output. num_attention_heads

r""" A feed-forward layer. Parameters: dim (`int`): The number of channels in the input. dim_out (`int`, *optional*): The num

r""" A variant of the gated linear unit activation function from https://arxiv.org/abs/2002.05202. Parameters: dim_in (`int`): The nu