ab_upt_config
=============

.. py:module:: ab_upt_config


Classes
-------

.. autoapisummary::

   ab_upt_config.AnchorBranchedUPTConfig


Module Contents
---------------

.. py:class:: AnchorBranchedUPTConfig(/, **data)

   Bases: :py:obj:`pydantic.BaseModel`


   !!! abstract "Usage Documentation"
       [Models](../concepts/models.md)

   A base class for creating Pydantic models.

   .. attribute:: __class_vars__

      The names of the class variables defined on the model.

   .. attribute:: __private_attributes__

      Metadata about the private attributes of the model.

   .. attribute:: __signature__

      The synthesized `__init__` [`Signature`][inspect.Signature] of the model.

   .. attribute:: __pydantic_complete__

      Whether model building is completed, or if there are still undefined fields.

   .. attribute:: __pydantic_core_schema__

      The core schema of the model.

   .. attribute:: __pydantic_custom_init__

      Whether the model has a custom `__init__` function.

   .. attribute:: __pydantic_decorators__

      Metadata containing the decorators defined on the model.
      This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.

   .. attribute:: __pydantic_generic_metadata__

      Metadata for generic models; contains data used for a similar purpose to
      __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

   .. attribute:: __pydantic_parent_namespace__

      Parent namespace of the model, used for automatic rebuilding of models.

   .. attribute:: __pydantic_post_init__

      The name of the post-init method for the model, if defined.

   .. attribute:: __pydantic_root_model__

      Whether the model is a [`RootModel`][pydantic.root_model.RootModel].

   .. attribute:: __pydantic_serializer__

      The `pydantic-core` `SchemaSerializer` used to dump instances of the model.

   .. attribute:: __pydantic_validator__

      The `pydantic-core` `SchemaValidator` used to validate instances of the model.

   .. attribute:: __pydantic_fields__

      A dictionary of field names and their corresponding [`FieldInfo`][pydantic.fields.FieldInfo] objects.

   .. attribute:: __pydantic_computed_fields__

      A dictionary of computed field names and their corresponding [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] objects.

   .. attribute:: __pydantic_extra__

      A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra]
      is set to `'allow'`.

   .. attribute:: __pydantic_fields_set__

      The names of fields explicitly set during instantiation.

   .. attribute:: __pydantic_private__

      Values of private attributes set on the model instance.

   Create a new model by parsing and validating input data from keyword arguments.

   Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
   validated to form a valid model.

   `self` is explicitly positional-only to allow `self` as a field name.


   .. py:attribute:: supernode_pooling_config
      :type:  emmi.schemas.modules.encoder.SupernodePoolingConfig


   .. py:attribute:: transformer_block_config
      :type:  emmi.schemas.modules.blocks.transformer_block_config.TransformerBlockConfig


   .. py:attribute:: geometry_depth
      :type:  int
      :value: None


      Number of transformer blocks in the geometry encoder.


   .. py:attribute:: hidden_dim
      :type:  int
      :value: None


      Hidden dimension of the model.


   .. py:attribute:: physics_blocks
      :type:  list[Literal['shared', 'cross', 'joint', 'perceiver']]

      Types of physics blocks to use in the model.
      Options are "shared", "cross", "joint", and "perceiver".
      Shared: Self-attention within a branch (surface or volume). Attention blocks share weights between surface and volume.
      Cross: Cross-attention between surface and volume branches. Weights are shared between surface and volume.
      Joint: Joint attention over surface and volume points. I.e. full self-attention over both surface and volume points.
      Perceiver: Perceiver-style attention blocks.


   .. py:attribute:: num_surface_blocks
      :type:  int
      :value: None


      Number of transformer blocks in the surface decoder. Weights are not shared with the volume decoder.


   .. py:attribute:: num_volume_blocks
      :type:  int
      :value: None


      Number of transformer blocks in the volume decoder. Weights are not shared with the surface decoder.


   .. py:attribute:: init_weights
      :type:  emmi.types.InitWeightsMode
      :value: None


      Weight initialization of linear layers. Defaults to "truncnormal002".


   .. py:attribute:: drop_path_rate
      :type:  float
      :value: None


      Drop path rate for stochastic depth. Defaults to 0.0 (no drop path).