A thin wrapper around neml2::Model
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TensorValue | __getattr__ (self, str arg0) |
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None | __setattr__ (self, str arg0, tensors.Tensor arg1) |
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str | __str__ (self) |
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dict[LabeledAxisAccessor, dict[LabeledAxisAccessor, dict[LabeledAxisAccessor, tensors.Tensor]]] | d2value (self, dict arg0) |
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dict[str, Model] | dependency (self) |
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dict[LabeledAxisAccessor, dict[LabeledAxisAccessor, tensors.Tensor]] | dvalue (self, dict arg0) |
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tuple[dict[LabeledAxisAccessor, dict[LabeledAxisAccessor, tensors.Tensor]], dict[LabeledAxisAccessor, dict[LabeledAxisAccessor, dict[LabeledAxisAccessor, tensors.Tensor]]]] | dvalue_and_d2value (self, dict arg0) |
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TensorValue | get_parameter (self, str arg0) |
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LabeledAxis | input_axis (self) |
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tensors.TensorType | input_type (self, LabeledAxisAccessor variable) |
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dict[str, TensorValue] | named_buffers (self) |
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dict[str, TensorValue] | named_parameters (self) |
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dict[str, Model] | named_submodels (self) |
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LabeledAxis | output_axis (self) |
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tensors.TensorType | output_type (self, LabeledAxisAccessor variable) |
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None | set_parameter (self, str arg0, tensors.Tensor arg1) |
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None | set_parameters (self, dict[str, tensors.Tensor] arg0) |
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None | to (self, *torch.dtype dtype=..., torch.device device=..., bool requires_grad=False) |
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dict[LabeledAxisAccessor, tensors.Tensor] | value (self, dict arg0) |
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tuple[dict[LabeledAxisAccessor, tensors.Tensor], dict[LabeledAxisAccessor, dict[LabeledAxisAccessor, tensors.Tensor]]] | value_and_dvalue (self, dict arg0) |
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tuple[dict[LabeledAxisAccessor, tensors.Tensor], dict[LabeledAxisAccessor, dict[LabeledAxisAccessor, tensors.Tensor]], dict[LabeledAxisAccessor, dict[LabeledAxisAccessor, dict[LabeledAxisAccessor, tensors.Tensor]]]] | value_and_dvalue_and_d2value (self, dict arg0) |
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str | name (self) |
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str | type (self) |
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