Instructions to use jonl521/modeling_simple with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonl521/modeling_simple with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jonl521/modeling_simple", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 974 Bytes
2216aae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | """ The ``sympy.codegen`` module contains classes and functions for building
abstract syntax trees of algorithms. These trees may then be printed by the
code-printers in ``sympy.printing``.
There are several submodules available:
- ``sympy.codegen.ast``: AST nodes useful across multiple languages.
- ``sympy.codegen.cnodes``: AST nodes useful for the C family of languages.
- ``sympy.codegen.fnodes``: AST nodes useful for Fortran.
- ``sympy.codegen.cfunctions``: functions specific to C (C99 math functions)
- ``sympy.codegen.ffunctions``: functions specific to Fortran (e.g. ``kind``).
"""
from .ast import (
Assignment, aug_assign, CodeBlock, For, Attribute, Variable, Declaration,
While, Scope, Print, FunctionPrototype, FunctionDefinition, FunctionCall
)
__all__ = [
'Assignment', 'aug_assign', 'CodeBlock', 'For', 'Attribute', 'Variable',
'Declaration', 'While', 'Scope', 'Print', 'FunctionPrototype',
'FunctionDefinition', 'FunctionCall',
]
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