(self, all_samples, suffix=0)

(config, epoch, model, optimizer, loss_scaler, checkpoint_paths)

(self, batch)

(self, batch)

(model, data_loader, optimizer, device, epoch, loss_scaler, log_writer=None, config=N

(self, inds)

(args, config)

(args, config)

(config, output_path)

(self, *args, **kwargs)

(self, loss, logits, targets)

(self, img)

(self, img)

(self, img)

(self, img1, img2)

(self, pred_imgs, gt_imgs)

(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False, update_grad=True)

(self, index)

(self, index)

(self, i)

(self, i)

(self)

(self)

(self)

(self)

(self, path='../dreamdiffusion/datasets/mne_data/', roi='VC', patch_size=16, transform=identity, aug_times=2,

(self, x, y=None, transform=identity)

(self, eeg_signals_path, imagenet_path, image_transform=identity, subject = 4)

(self, size, p)

(self, size, p)

(self, config)

(self, size, p)

(self)

(self)

(self)

(self, time_len=224, patch_size=1, in_chans=128, embed_dim=256)

(self, time_len=512, patch_size=4, embed_dim=1024, in_chans=128, depth=24, num_heads=16, deco

(self, time_len=512, patch_size=4, embed_dim=1024, in_chans=128, depth=24, num_heads=16, mlp_

(self)

(self, metafile, num_voxels=440, cond_dim=1280, global_pool=True, clip_tune = True, cls_tune = False)

(self, config_path, num_voxels, device=torch.device('cpu'), pretrain_root='../pretrains/',

(self, model, decay=0.9999, use_num_upates=True)

(self, dim, max_seq_len)

(self, dim)

(self, fn)

(self, dim, eps=1e-5)

(self, dim, eps=1e-8)

(self, dim)

(self, dim_in, dim_out)

(self, dim, dim_out=None, mult=4, glu=False, dropout=0.)

( self, dim, dim_head=DEFAULT_DIM_HEAD, heads=8, c

( self, dim, depth, heads=8, causal=False,

(self, **kwargs)

( self, *, num_tokens, max_seq_len, attn_layers,

(self, dim, dim_out=None, mult=4, glu=False, dropout=0.)

(self, dim, heads=4, dim_head=32)

(self, in_channels)

(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., cond_scale=1.)

(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, cond_scale=1.)

(self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None, cond_scale=1.)

( self, spacial_dim: int, embed_dim: int, num_heads_channels: int, out

(self, channels, out_channels=None, ks=5)

(self, channels, use_conv, dims=2, out_channels=None,padding=1)

( self, channels, emb_channels, dropout, out_channels=None, us

( self, channels, num_heads=1, num_head_channels=-1, use_checkpoint=Fa

(self, n_heads)

(self, n_heads)

( self, image_size, in_channels, model_channels, out_channels,

( self, image_size, in_channels, model_channels, out_channels,

(self, c_concat_config, c_crossattn_config)

(self, in_channels, with_conv)

(self, in_channels, with_conv)

(self, *, in_channels, out_channels=None, conv_shortcut=False, dropout, temb_channels=512)

(self, in_channels)

(self, in_channels)

(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resam

(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resam

(self, in_channels, out_channels, *args, **kwargs)

(self, in_channels, out_channels, ch, num_res_blocks, resolution, ch_mult=(2,2), dropout=0.0)

(self, factor, in_channels, mid_channels, out_channels, depth=2)

(self, in_channels, ch, resolution, out_ch, num_res_blocks, attn_resolutions, dropout=0.0, re

(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),

(self, in_size, out_size, in_channels, out_channels, ch_mult=2)

(self, in_channels=None, learned=False, mode="bilinear")

(self, ch_mult:list, in_channels, pretrained_model:nn.Module=None, reshape=F

(self, value)

(self, parameters, deterministic=False)

(self)

(self, embed_dim, n_classes=1000, key='class')

(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda")

(self, device="cuda", vq_interface=True, max_length=77)

(self, n_stages=1, method='bilinear', multiplier=0.5,

(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77)

(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77)

(self, version='ViT-L/14', device="cuda", max_length=77, n_repeat=1, normalize=True)

( self, model = 'ViT-L/14', jit=False, device='cuda' if torch.

(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0, disc_num_layers=3, disc_in_chan

(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0, disc_num_layers=3, d

(self, n_e, e_dim, beta, remap=None, unknown_index="random", sane_index_shape=False, legacy=T