task stringclasses 4
values | prompt stringlengths 60 573k | completion stringlengths 60 683k | source_language stringclasses 2
values | target_language stringclasses 4
values | run_name stringclasses 2
values | example_index int64 1 100k | source_metadata dict | hashes dict | source_compile_ok bool 1
class | roundtrip_compile_ok bool 1
class | runtime_signature_match null |
|---|---|---|---|---|---|---|---|---|---|---|---|
python_to_en_plaincode | class Solution:
def finalPrices(self, prices: List[int]) -> List[int]:
res = []
for i in range(len(prices)):
for j in range(i+1,len(prices)):
if prices[j]<=prices[i]:
res.append(prices[i]-prices[j])
break
if j==len(p... | Define class Solution:
Define method finalPrices with parameters self, prices (type: List of int); return type List of int:
Set res to an empty list.
For each i in the range starting at 0 and stopping before len with prices:
For each j in the range starting at i plus 1 and stopping befor... | python | en | run_002_20260417_060406 | 1 | {
"max_stars_repo_path": "coding_intereview/1475. Final Prices With a Special Discount in a Shop.py",
"max_stars_repo_name": "Jahidul007/Python-Bootcamp",
"max_stars_count": 2,
"id": "3",
"raw_source_hash": "a7d86c06e5f0bd5932f94342ef1cd1419c14922f3e3bfeae0ce44b4dcda06eae",
"sanitize_meta": {
"triple_bl... | {
"raw_source_hash": "a7d86c06e5f0bd5932f94342ef1cd1419c14922f3e3bfeae0ce44b4dcda06eae",
"normalized_source_hash": "fb41c52b1740ad3039a1bbc7a3f7e79c56a36e746967aa1e1e153776a73f8193",
"source_ast_hash": "e28d606b7dbd1a2470550f821bcef517258cae14c641f9a7eb001a4363f416c1",
"artifact_hash": "44830b93a9ddbbf7631cb76f... | true | true | null |
python_to_es_plaincode | class Solution:
def finalPrices(self, prices: List[int]) -> List[int]:
res = []
for i in range(len(prices)):
for j in range(i+1,len(prices)):
if prices[j]<=prices[i]:
res.append(prices[i]-prices[j])
break
if j==len(p... | Definir clase Solution:
Definir método finalPrices con parámetros self, prices (tipo: List de int); tipo de retorno List de int:
Establecer res como una lista vacía.
Para cada i en el rango que comienza en 0 y también deteniéndose antes de len con prices:
Para cada j en el rango que comi... | python | es | run_002_20260417_060406 | 1 | {
"max_stars_repo_path": "coding_intereview/1475. Final Prices With a Special Discount in a Shop.py",
"max_stars_repo_name": "Jahidul007/Python-Bootcamp",
"max_stars_count": 2,
"id": "3",
"raw_source_hash": "a7d86c06e5f0bd5932f94342ef1cd1419c14922f3e3bfeae0ce44b4dcda06eae",
"sanitize_meta": {
"triple_bl... | {
"raw_source_hash": "a7d86c06e5f0bd5932f94342ef1cd1419c14922f3e3bfeae0ce44b4dcda06eae",
"normalized_source_hash": "fb41c52b1740ad3039a1bbc7a3f7e79c56a36e746967aa1e1e153776a73f8193",
"source_ast_hash": "e28d606b7dbd1a2470550f821bcef517258cae14c641f9a7eb001a4363f416c1",
"artifact_hash": "44830b93a9ddbbf7631cb76f... | true | true | null |
python_to_fr_plaincode | class Solution:
def finalPrices(self, prices: List[int]) -> List[int]:
res = []
for i in range(len(prices)):
for j in range(i+1,len(prices)):
if prices[j]<=prices[i]:
res.append(prices[i]-prices[j])
break
if j==len(p... | Définir classe Solution:
Définir méthode finalPrices avec paramètres self, prices (type : List de int); type de retour List de int:
Affecter res à une liste vide.
Pour chaque i dans la plage commençant à 0 et s'arrêtant avant len avec prices:
Pour chaque j dans la plage commençant à i pl... | python | fr | run_002_20260417_060406 | 1 | {
"max_stars_repo_path": "coding_intereview/1475. Final Prices With a Special Discount in a Shop.py",
"max_stars_repo_name": "Jahidul007/Python-Bootcamp",
"max_stars_count": 2,
"id": "3",
"raw_source_hash": "a7d86c06e5f0bd5932f94342ef1cd1419c14922f3e3bfeae0ce44b4dcda06eae",
"sanitize_meta": {
"triple_bl... | {
"raw_source_hash": "a7d86c06e5f0bd5932f94342ef1cd1419c14922f3e3bfeae0ce44b4dcda06eae",
"normalized_source_hash": "fb41c52b1740ad3039a1bbc7a3f7e79c56a36e746967aa1e1e153776a73f8193",
"source_ast_hash": "e28d606b7dbd1a2470550f821bcef517258cae14c641f9a7eb001a4363f416c1",
"artifact_hash": "44830b93a9ddbbf7631cb76f... | true | true | null |
en_plaincode_to_python | Define class Solution:
Define method finalPrices with parameters self, prices (type: List of int); return type List of int:
Set res to an empty list.
For each i in the range starting at 0 and stopping before len with prices:
For each j in the range starting at i plus 1 and stopping befor... | class Solution:
def finalPrices(self, prices: List[int]) -> List[int]:
res = []
for i in range(len(prices)):
for j in range(i+1,len(prices)):
if prices[j]<=prices[i]:
res.append(prices[i]-prices[j])
break
if j==len(p... | en | python | run_002_20260417_060406 | 1 | {
"max_stars_repo_path": "coding_intereview/1475. Final Prices With a Special Discount in a Shop.py",
"max_stars_repo_name": "Jahidul007/Python-Bootcamp",
"max_stars_count": 2,
"id": "3",
"raw_source_hash": "a7d86c06e5f0bd5932f94342ef1cd1419c14922f3e3bfeae0ce44b4dcda06eae",
"sanitize_meta": {
"triple_bl... | {
"raw_source_hash": "a7d86c06e5f0bd5932f94342ef1cd1419c14922f3e3bfeae0ce44b4dcda06eae",
"normalized_source_hash": "fb41c52b1740ad3039a1bbc7a3f7e79c56a36e746967aa1e1e153776a73f8193",
"source_ast_hash": "e28d606b7dbd1a2470550f821bcef517258cae14c641f9a7eb001a4363f416c1",
"artifact_hash": "44830b93a9ddbbf7631cb76f... | true | true | null |
python_to_en_plaincode | from __future__ import unicode_literals
from . import __version__ as app_version
app_name = "pos_kiosk"
app_title = "Pos Kiosk"
app_publisher = "9t9it"
app_description = "Kiosk App"
app_icon = "octicon octicon-file-directory"
app_color = "grey"
app_email = "<EMAIL>"
app_license = "MIT"
# Includes in <head>
# --------... | Load unicode_literals from __future__.
Load __version__ referred to as app_version from the current package.
Set app_name to "pos_kiosk".
Set app_title to "Pos Kiosk".
Set app_publisher to "9t9it".
Set app_description to "Kiosk App".
Set app_icon to "octicon octicon-file-directory".
Set app_color to "grey".
Set app_ema... | python | en | run_002_20260417_060406 | 2 | {
"max_stars_repo_path": "pos_kiosk/hooks.py",
"max_stars_repo_name": "Muzzy73/pos_kiosk",
"max_stars_count": 1,
"id": "6",
"raw_source_hash": "a5c711a4abcb2ecb962e46063669e0cb75044793206ec6adde1964cb67cf8b9f",
"sanitize_meta": {
"triple_block_count": 0,
"total_triple_chars": 0,
"largest_triple_... | {
"raw_source_hash": "a5c711a4abcb2ecb962e46063669e0cb75044793206ec6adde1964cb67cf8b9f",
"normalized_source_hash": "7029af563df358ab5cbc6d3961ae61ab13a4c79088f55f833d1993f758155b84",
"source_ast_hash": "00979410a8f5f72e075bf9d5bf06abaa21337279a8a1aac9efa32b2fba7dbfc2",
"artifact_hash": "c4b5525b6aa01ca71066a244... | true | true | null |
python_to_es_plaincode | from __future__ import unicode_literals
from . import __version__ as app_version
app_name = "pos_kiosk"
app_title = "Pos Kiosk"
app_publisher = "9t9it"
app_description = "Kiosk App"
app_icon = "octicon octicon-file-directory"
app_color = "grey"
app_email = "<EMAIL>"
app_license = "MIT"
# Includes in <head>
# --------... | Importar unicode_literals desde __future__.
Importar __version__ referido como app_version desde el paquete actual.
Establecer app_name como "pos_kiosk".
Establecer app_title como "Pos Kiosk".
Establecer app_publisher como "9t9it".
Establecer app_description como "Kiosk App".
Establecer app_icon como "octicon octicon-f... | python | es | run_002_20260417_060406 | 2 | {
"max_stars_repo_path": "pos_kiosk/hooks.py",
"max_stars_repo_name": "Muzzy73/pos_kiosk",
"max_stars_count": 1,
"id": "6",
"raw_source_hash": "a5c711a4abcb2ecb962e46063669e0cb75044793206ec6adde1964cb67cf8b9f",
"sanitize_meta": {
"triple_block_count": 0,
"total_triple_chars": 0,
"largest_triple_... | {
"raw_source_hash": "a5c711a4abcb2ecb962e46063669e0cb75044793206ec6adde1964cb67cf8b9f",
"normalized_source_hash": "7029af563df358ab5cbc6d3961ae61ab13a4c79088f55f833d1993f758155b84",
"source_ast_hash": "00979410a8f5f72e075bf9d5bf06abaa21337279a8a1aac9efa32b2fba7dbfc2",
"artifact_hash": "c4b5525b6aa01ca71066a244... | true | true | null |
python_to_fr_plaincode | from __future__ import unicode_literals
from . import __version__ as app_version
app_name = "pos_kiosk"
app_title = "Pos Kiosk"
app_publisher = "9t9it"
app_description = "Kiosk App"
app_icon = "octicon octicon-file-directory"
app_color = "grey"
app_email = "<EMAIL>"
app_license = "MIT"
# Includes in <head>
# --------... | Charger unicode_literals depuis __future__.
Charger __version__ référé comme app_version depuis le paquet actuel.
Affecter app_name à "pos_kiosk".
Affecter app_title à "Pos Kiosk".
Affecter app_publisher à "9t9it".
Affecter app_description à "Kiosk App".
Affecter app_icon à "octicon octicon-file-directory".
Affecter ap... | python | fr | run_002_20260417_060406 | 2 | {
"max_stars_repo_path": "pos_kiosk/hooks.py",
"max_stars_repo_name": "Muzzy73/pos_kiosk",
"max_stars_count": 1,
"id": "6",
"raw_source_hash": "a5c711a4abcb2ecb962e46063669e0cb75044793206ec6adde1964cb67cf8b9f",
"sanitize_meta": {
"triple_block_count": 0,
"total_triple_chars": 0,
"largest_triple_... | {
"raw_source_hash": "a5c711a4abcb2ecb962e46063669e0cb75044793206ec6adde1964cb67cf8b9f",
"normalized_source_hash": "7029af563df358ab5cbc6d3961ae61ab13a4c79088f55f833d1993f758155b84",
"source_ast_hash": "00979410a8f5f72e075bf9d5bf06abaa21337279a8a1aac9efa32b2fba7dbfc2",
"artifact_hash": "c4b5525b6aa01ca71066a244... | true | true | null |
en_plaincode_to_python | Load unicode_literals from __future__.
Load __version__ referred to as app_version from the current package.
Set app_name to "pos_kiosk".
Set app_title to "Pos Kiosk".
Set app_publisher to "9t9it".
Set app_description to "Kiosk App".
Set app_icon to "octicon octicon-file-directory".
Set app_color to "grey".
Set app_ema... | from __future__ import unicode_literals
from . import __version__ as app_version
app_name = "pos_kiosk"
app_title = "Pos Kiosk"
app_publisher = "9t9it"
app_description = "Kiosk App"
app_icon = "octicon octicon-file-directory"
app_color = "grey"
app_email = "<EMAIL>"
app_license = "MIT"
# Includes in <head>
# --------... | en | python | run_002_20260417_060406 | 2 | {
"max_stars_repo_path": "pos_kiosk/hooks.py",
"max_stars_repo_name": "Muzzy73/pos_kiosk",
"max_stars_count": 1,
"id": "6",
"raw_source_hash": "a5c711a4abcb2ecb962e46063669e0cb75044793206ec6adde1964cb67cf8b9f",
"sanitize_meta": {
"triple_block_count": 0,
"total_triple_chars": 0,
"largest_triple_... | {
"raw_source_hash": "a5c711a4abcb2ecb962e46063669e0cb75044793206ec6adde1964cb67cf8b9f",
"normalized_source_hash": "7029af563df358ab5cbc6d3961ae61ab13a4c79088f55f833d1993f758155b84",
"source_ast_hash": "00979410a8f5f72e075bf9d5bf06abaa21337279a8a1aac9efa32b2fba7dbfc2",
"artifact_hash": "c4b5525b6aa01ca71066a244... | true | true | null |
python_to_en_plaincode | from keras import Model, Input
from keras.layers import Dense, concatenate, LSTM, Reshape, Permute, Embedding, Dropout, Convolution1D, Flatten
from keras.optimizers import Adam
from pypagai.models.base import KerasModel
class SimpleLSTM(KerasModel):
"""
Use a simple lstm neural network
"""
@staticmet... | Load Model, Input from keras.
Load Dense, concatenate, LSTM, Reshape, Permute, Embedding, Dropout, Convolution1D, Flatten from keras.layers.
Load Adam from keras.optimizers.
Load KerasModel from pypagai.models.base.
Define class SimpleLSTM inheriting from KerasModel:
Text block:
""
" Use a simple lstm ne... | python | en | run_002_20260417_060406 | 3 | {
"max_stars_repo_path": "pypagai/models/model_lstm.py",
"max_stars_repo_name": "gcouti/pypagAI",
"max_stars_count": 1,
"id": "7",
"raw_source_hash": "6ff5eb1643faabfe79e7cf12b3b77bdafbf223256b942b1d5db7a26437ee9a32",
"sanitize_meta": {
"triple_block_count": 3,
"total_triple_chars": 144,
"larges... | {
"raw_source_hash": "6ff5eb1643faabfe79e7cf12b3b77bdafbf223256b942b1d5db7a26437ee9a32",
"normalized_source_hash": "85eb10c40cbf0b55fd2e8845ce3f9f0ef240c6c527f9c39a923b896640d00f57",
"source_ast_hash": "3947ecf078c1470508bc0f6ef53482e2d7796c6ee7babb8a53c9c99a71189db0",
"artifact_hash": "8b70f39afd85f9911ab0240d... | true | true | null |
python_to_es_plaincode | from keras import Model, Input
from keras.layers import Dense, concatenate, LSTM, Reshape, Permute, Embedding, Dropout, Convolution1D, Flatten
from keras.optimizers import Adam
from pypagai.models.base import KerasModel
class SimpleLSTM(KerasModel):
"""
Use a simple lstm neural network
"""
@staticmet... | Importar Model, Input desde keras.
Importar Dense, concatenate, LSTM, Reshape, Permute, Embedding, Dropout, Convolution1D, Flatten desde keras.layers.
Importar Adam desde keras.optimizers.
Importar KerasModel desde pypagai.models.base.
Definir clase SimpleLSTM heredando de KerasModel:
Texto literal:
""
" ... | python | es | run_002_20260417_060406 | 3 | {
"max_stars_repo_path": "pypagai/models/model_lstm.py",
"max_stars_repo_name": "gcouti/pypagAI",
"max_stars_count": 1,
"id": "7",
"raw_source_hash": "6ff5eb1643faabfe79e7cf12b3b77bdafbf223256b942b1d5db7a26437ee9a32",
"sanitize_meta": {
"triple_block_count": 3,
"total_triple_chars": 144,
"larges... | {
"raw_source_hash": "6ff5eb1643faabfe79e7cf12b3b77bdafbf223256b942b1d5db7a26437ee9a32",
"normalized_source_hash": "85eb10c40cbf0b55fd2e8845ce3f9f0ef240c6c527f9c39a923b896640d00f57",
"source_ast_hash": "3947ecf078c1470508bc0f6ef53482e2d7796c6ee7babb8a53c9c99a71189db0",
"artifact_hash": "8b70f39afd85f9911ab0240d... | true | true | null |
python_to_fr_plaincode | from keras import Model, Input
from keras.layers import Dense, concatenate, LSTM, Reshape, Permute, Embedding, Dropout, Convolution1D, Flatten
from keras.optimizers import Adam
from pypagai.models.base import KerasModel
class SimpleLSTM(KerasModel):
"""
Use a simple lstm neural network
"""
@staticmet... | Charger Model, Input depuis keras.
Charger Dense, concatenate, LSTM, Reshape, Permute, Embedding, Dropout, Convolution1D, Flatten depuis keras.layers.
Charger Adam depuis keras.optimizers.
Charger KerasModel depuis pypagai.models.base.
Définir classe SimpleLSTM héritant de KerasModel:
Texte littéral:
""
" ... | python | fr | run_002_20260417_060406 | 3 | {
"max_stars_repo_path": "pypagai/models/model_lstm.py",
"max_stars_repo_name": "gcouti/pypagAI",
"max_stars_count": 1,
"id": "7",
"raw_source_hash": "6ff5eb1643faabfe79e7cf12b3b77bdafbf223256b942b1d5db7a26437ee9a32",
"sanitize_meta": {
"triple_block_count": 3,
"total_triple_chars": 144,
"larges... | {
"raw_source_hash": "6ff5eb1643faabfe79e7cf12b3b77bdafbf223256b942b1d5db7a26437ee9a32",
"normalized_source_hash": "85eb10c40cbf0b55fd2e8845ce3f9f0ef240c6c527f9c39a923b896640d00f57",
"source_ast_hash": "3947ecf078c1470508bc0f6ef53482e2d7796c6ee7babb8a53c9c99a71189db0",
"artifact_hash": "8b70f39afd85f9911ab0240d... | true | true | null |
en_plaincode_to_python | Load Model, Input from keras.
Load Dense, concatenate, LSTM, Reshape, Permute, Embedding, Dropout, Convolution1D, Flatten from keras.layers.
Load Adam from keras.optimizers.
Load KerasModel from pypagai.models.base.
Define class SimpleLSTM inheriting from KerasModel:
Text block:
""
" Use a simple lstm ne... | from keras import Model, Input
from keras.layers import Dense, concatenate, LSTM, Reshape, Permute, Embedding, Dropout, Convolution1D, Flatten
from keras.optimizers import Adam
from pypagai.models.base import KerasModel
class SimpleLSTM(KerasModel):
"""
Use a simple lstm neural network
"""
@staticmet... | en | python | run_002_20260417_060406 | 3 | {
"max_stars_repo_path": "pypagai/models/model_lstm.py",
"max_stars_repo_name": "gcouti/pypagAI",
"max_stars_count": 1,
"id": "7",
"raw_source_hash": "6ff5eb1643faabfe79e7cf12b3b77bdafbf223256b942b1d5db7a26437ee9a32",
"sanitize_meta": {
"triple_block_count": 3,
"total_triple_chars": 144,
"larges... | {
"raw_source_hash": "6ff5eb1643faabfe79e7cf12b3b77bdafbf223256b942b1d5db7a26437ee9a32",
"normalized_source_hash": "85eb10c40cbf0b55fd2e8845ce3f9f0ef240c6c527f9c39a923b896640d00f57",
"source_ast_hash": "3947ecf078c1470508bc0f6ef53482e2d7796c6ee7babb8a53c9c99a71189db0",
"artifact_hash": "8b70f39afd85f9911ab0240d... | true | true | null |
python_to_en_plaincode | # Author:
''' PNASNet in PyTorch.
Paper: Progressive Neural Architecture Search
'''
from easyai.base_name.block_name import NormalizationType, ActivationType
from easyai.base_name.backbone_name import BackboneName
from easyai.model.backbone.utility.base_backbone import *
from easyai.model.base_block.utility.utility_bl... | # Author:
Text block:
" PNASNet in PyTorch."
"Paper: Progressive Neural Architecture Search"
ending with a newline.
Load NormalizationType, ActivationType from easyai.base_name.block_name.
Load BackboneName from easyai.base_name.backbone_name.
Load everything from easyai.model.backbone.utility.base_backbone.
Load ConvB... | python | en | run_002_20260417_060406 | 4 | {
"max_stars_repo_path": "easyai/model/backbone/cls/pnasnet.py",
"max_stars_repo_name": "lpj0822/image_point_cloud_det",
"max_stars_count": 1,
"id": "9",
"raw_source_hash": "1e7ae79e5a84953e5230479e541e934f228111b32411a607f7b5903ae33da37f",
"sanitize_meta": {
"triple_block_count": 1,
"total_triple_c... | {
"raw_source_hash": "1e7ae79e5a84953e5230479e541e934f228111b32411a607f7b5903ae33da37f",
"normalized_source_hash": "824e0336848577e6a1b3741ebb94a816e1d6ba123bedc819b84655610998cec7",
"source_ast_hash": "71ba39b897ee88d72350b3bd41d5e4fcd8ec68a55fbaf588cf46099332400597",
"artifact_hash": "8d55ceb200088270bb9f0dc4... | true | true | null |
python_to_es_plaincode | # Author:
''' PNASNet in PyTorch.
Paper: Progressive Neural Architecture Search
'''
from easyai.base_name.block_name import NormalizationType, ActivationType
from easyai.base_name.backbone_name import BackboneName
from easyai.model.backbone.utility.base_backbone import *
from easyai.model.base_block.utility.utility_bl... | # Author:
Texto literal:
" PNASNet in PyTorch."
"Paper: Progressive Neural Architecture Search"
terminando con una nueva línea.
Importar NormalizationType, ActivationType desde easyai.base_name.block_name.
Importar BackboneName desde easyai.base_name.backbone_name.
Importar todo desde easyai.model.backbone.utility.base... | python | es | run_002_20260417_060406 | 4 | {
"max_stars_repo_path": "easyai/model/backbone/cls/pnasnet.py",
"max_stars_repo_name": "lpj0822/image_point_cloud_det",
"max_stars_count": 1,
"id": "9",
"raw_source_hash": "1e7ae79e5a84953e5230479e541e934f228111b32411a607f7b5903ae33da37f",
"sanitize_meta": {
"triple_block_count": 1,
"total_triple_c... | {
"raw_source_hash": "1e7ae79e5a84953e5230479e541e934f228111b32411a607f7b5903ae33da37f",
"normalized_source_hash": "824e0336848577e6a1b3741ebb94a816e1d6ba123bedc819b84655610998cec7",
"source_ast_hash": "71ba39b897ee88d72350b3bd41d5e4fcd8ec68a55fbaf588cf46099332400597",
"artifact_hash": "8d55ceb200088270bb9f0dc4... | true | true | null |
python_to_fr_plaincode | # Author:
''' PNASNet in PyTorch.
Paper: Progressive Neural Architecture Search
'''
from easyai.base_name.block_name import NormalizationType, ActivationType
from easyai.base_name.backbone_name import BackboneName
from easyai.model.backbone.utility.base_backbone import *
from easyai.model.base_block.utility.utility_bl... | # Author:
Texte littéral:
" PNASNet in PyTorch."
"Paper: Progressive Neural Architecture Search"
se terminant par une nouvelle ligne.
Charger NormalizationType, ActivationType depuis easyai.base_name.block_name.
Charger BackboneName depuis easyai.base_name.backbone_name.
Charger tout depuis easyai.model.backbone.utilit... | python | fr | run_002_20260417_060406 | 4 | {
"max_stars_repo_path": "easyai/model/backbone/cls/pnasnet.py",
"max_stars_repo_name": "lpj0822/image_point_cloud_det",
"max_stars_count": 1,
"id": "9",
"raw_source_hash": "1e7ae79e5a84953e5230479e541e934f228111b32411a607f7b5903ae33da37f",
"sanitize_meta": {
"triple_block_count": 1,
"total_triple_c... | {
"raw_source_hash": "1e7ae79e5a84953e5230479e541e934f228111b32411a607f7b5903ae33da37f",
"normalized_source_hash": "824e0336848577e6a1b3741ebb94a816e1d6ba123bedc819b84655610998cec7",
"source_ast_hash": "71ba39b897ee88d72350b3bd41d5e4fcd8ec68a55fbaf588cf46099332400597",
"artifact_hash": "8d55ceb200088270bb9f0dc4... | true | true | null |
en_plaincode_to_python | # Author:
Text block:
" PNASNet in PyTorch."
"Paper: Progressive Neural Architecture Search"
ending with a newline.
Load NormalizationType, ActivationType from easyai.base_name.block_name.
Load BackboneName from easyai.base_name.backbone_name.
Load everything from easyai.model.backbone.utility.base_backbone.
Load ConvB... | # Author:
''' PNASNet in PyTorch.
Paper: Progressive Neural Architecture Search
'''
from easyai.base_name.block_name import NormalizationType, ActivationType
from easyai.base_name.backbone_name import BackboneName
from easyai.model.backbone.utility.base_backbone import *
from easyai.model.base_block.utility.utility_bl... | en | python | run_002_20260417_060406 | 4 | {
"max_stars_repo_path": "easyai/model/backbone/cls/pnasnet.py",
"max_stars_repo_name": "lpj0822/image_point_cloud_det",
"max_stars_count": 1,
"id": "9",
"raw_source_hash": "1e7ae79e5a84953e5230479e541e934f228111b32411a607f7b5903ae33da37f",
"sanitize_meta": {
"triple_block_count": 1,
"total_triple_c... | {
"raw_source_hash": "1e7ae79e5a84953e5230479e541e934f228111b32411a607f7b5903ae33da37f",
"normalized_source_hash": "824e0336848577e6a1b3741ebb94a816e1d6ba123bedc819b84655610998cec7",
"source_ast_hash": "71ba39b897ee88d72350b3bd41d5e4fcd8ec68a55fbaf588cf46099332400597",
"artifact_hash": "8d55ceb200088270bb9f0dc4... | true | true | null |
python_to_en_plaincode | import json
import os
import math
import logging
import requests
import time
from map_download.cmd.BaseDownloader import DownloadEngine, BaseDownloaderThread, latlng2tile_terrain, BoundBox
def get_access_token(token):
resp = None
request_count = 0
url = "https://api.cesium.com/v1/assets/1/endpoint"
w... | Load json.
Load os.
Load math.
Load logging.
Load requests.
Load time.
Load DownloadEngine, BaseDownloaderThread, latlng2tile_terrain, BoundBox from map_download.cmd.BaseDownloader.
Define function get_access_token with parameter token:
Set resp to None.
Set request_count to 0.
Set url to "https://api.cesiu... | python | en | run_002_20260417_060406 | 5 | {
"max_stars_repo_path": "map_download/cmd/TerrainDownloader.py",
"max_stars_repo_name": "cugxy/map_download",
"max_stars_count": 27,
"id": "10",
"raw_source_hash": "8ba343fa3627630456d4d8758a7ce2f4bc8e55f923f2ee88df259080c5cad8d4",
"sanitize_meta": {
"triple_block_count": 0,
"total_triple_chars": 0... | {
"raw_source_hash": "8ba343fa3627630456d4d8758a7ce2f4bc8e55f923f2ee88df259080c5cad8d4",
"normalized_source_hash": "735d910720bc9acc94329223be31bf3b0b8f4817bb6aaa80c0fa5192533c2028",
"source_ast_hash": "8fd78bde1199004c7b06339b47de5eab9bb9ad06dc8980f9df96eef79a5d73fe",
"artifact_hash": "7544fde26507442a0aac483d... | true | true | null |
python_to_es_plaincode | import json
import os
import math
import logging
import requests
import time
from map_download.cmd.BaseDownloader import DownloadEngine, BaseDownloaderThread, latlng2tile_terrain, BoundBox
def get_access_token(token):
resp = None
request_count = 0
url = "https://api.cesium.com/v1/assets/1/endpoint"
w... | Importar json.
Importar os.
Importar math.
Importar logging.
Importar requests.
Importar time.
Importar DownloadEngine, BaseDownloaderThread, latlng2tile_terrain, BoundBox desde map_download.cmd.BaseDownloader.
Definir función get_access_token con parámetro token:
Establecer resp como None.
Establecer request_c... | python | es | run_002_20260417_060406 | 5 | {
"max_stars_repo_path": "map_download/cmd/TerrainDownloader.py",
"max_stars_repo_name": "cugxy/map_download",
"max_stars_count": 27,
"id": "10",
"raw_source_hash": "8ba343fa3627630456d4d8758a7ce2f4bc8e55f923f2ee88df259080c5cad8d4",
"sanitize_meta": {
"triple_block_count": 0,
"total_triple_chars": 0... | {
"raw_source_hash": "8ba343fa3627630456d4d8758a7ce2f4bc8e55f923f2ee88df259080c5cad8d4",
"normalized_source_hash": "735d910720bc9acc94329223be31bf3b0b8f4817bb6aaa80c0fa5192533c2028",
"source_ast_hash": "8fd78bde1199004c7b06339b47de5eab9bb9ad06dc8980f9df96eef79a5d73fe",
"artifact_hash": "7544fde26507442a0aac483d... | true | true | null |
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