SYMBOL INDEX (53 symbols across 5 files)

FILE: clip/clip.py
  function _download (line 43) | def _download(url: str, root: str):
  function _convert_image_to_rgb (line 75) | def _convert_image_to_rgb(image):
  function _transform (line 79) | def _transform(n_px):
  function available_models (line 89) | def available_models() -> List[str]:
  function load (line 94) | def load(name: str, device: Union[str, torch.device] = "cuda" if torch.c...
  function tokenize (line 205) | def tokenize(texts: Union[str, List[str]], context_length: int = 77, tru...

FILE: clip/model.py
  class Bottleneck (line 10) | class Bottleneck(nn.Module):
    method __init__ (line 13) | def __init__(self, inplanes, planes, stride=1):
    method forward (line 42) | def forward(self, x: torch.Tensor):
  class AttentionPool2d (line 58) | class AttentionPool2d(nn.Module):
    method __init__ (line 59) | def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, o...
    method forward (line 68) | def forward(self, x):
  class ModifiedResNet (line 94) | class ModifiedResNet(nn.Module):
    method __init__ (line 102) | def __init__(self, layers, output_dim, heads, input_resolution=224, wi...
    method _make_layer (line 129) | def _make_layer(self, planes, blocks, stride=1):
    method forward (line 138) | def forward(self, x):
  class LayerNorm (line 157) | class LayerNorm(nn.LayerNorm):
    method forward (line 160) | def forward(self, x: torch.Tensor):
  class QuickGELU (line 166) | class QuickGELU(nn.Module):
    method forward (line 167) | def forward(self, x: torch.Tensor):
  class ResidualAttentionBlock (line 171) | class ResidualAttentionBlock(nn.Module):
    method __init__ (line 172) | def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor ...
    method attention (line 185) | def attention(self, x: torch.Tensor):
    method forward (line 189) | def forward(self, x: torch.Tensor):
  class Transformer (line 195) | class Transformer(nn.Module):
    method __init__ (line 196) | def __init__(self, width: int, layers: int, heads: int, attn_mask: tor...
    method forward (line 202) | def forward(self, x: torch.Tensor):
  class VisionTransformer (line 206) | class VisionTransformer(nn.Module):
    method __init__ (line 207) | def __init__(self, input_resolution: int, patch_size: int, width: int,...
    method forward (line 223) | def forward(self, x: torch.Tensor):
  class CLIP (line 243) | class CLIP(nn.Module):
    method __init__ (line 244) | def __init__(self,
    method initialize_parameters (line 299) | def initialize_parameters(self):
    method build_attention_mask (line 328) | def build_attention_mask(self):
    method dtype (line 337) | def dtype(self):
    method encode_image (line 340) | def encode_image(self, image):
    method encode_text (line 343) | def encode_text(self, text):
    method forward (line 358) | def forward(self, image, text):
  function convert_weights (line 375) | def convert_weights(model: nn.Module):
  function build_model (line 399) | def build_model(state_dict: dict):

FILE: clip/simple_tokenizer.py
  function default_bpe (line 11) | def default_bpe():
  function bytes_to_unicode (line 16) | def bytes_to_unicode():
  function get_pairs (line 38) | def get_pairs(word):
  function basic_clean (line 50) | def basic_clean(text):
  function whitespace_clean (line 56) | def whitespace_clean(text):
  class SimpleTokenizer (line 62) | class SimpleTokenizer(object):
    method __init__ (line 63) | def __init__(self, bpe_path: str = default_bpe()):
    method bpe (line 80) | def bpe(self, token):
    method encode (line 121) | def encode(self, text):
    method decode (line 129) | def decode(self, tokens):

FILE: hubconf.py
  function _create_hub_entrypoint (line 10) | def _create_hub_entrypoint(model):
  function tokenize (line 37) | def tokenize():

FILE: tests/test_consistency.py
  function test_consistency (line 10) | def test_consistency(model_name):