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NEML2 2.0.0
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Construct a R2 with a vector of Scalars. The vector length must be 1, 3, 6, or 9. When vector length is 1, the Scalar value is used to fill the diagonals; when vector length is 3, the Scalar values are used to fill the respective diagonal entries; when vector length is 6, the Scalar values are used to fill the tensor following the Voigt notation; when vector length is 9, the Scalar values are used to fill the tensor in the row-major fashion.
shape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalues Scalars used to fill the R2Detailed documentation link
Construct a Rot from a vector of Scalars.
method Fill method, options are 'modified' and 'standard'.shape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalues Scalars used to fill the RotDetailed documentation link
Construct a SR2 with a vector of Scalars. The vector length must be 1, 3, or 6. See the full documentation on neml2::SR2 on the dispatch of fill method. When vector length is 1, the Scalar value is used to fill the diagonals; when vector length is 3, the Scalar values are used to fill the respective diagonal entries; when vector length is 6, the Scalar values are used to fill the tensor following the Voigt notation.
shape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalues Scalars used to fill the SR2Detailed documentation link
Construct a WR2 from a vector of Scalars.
shape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalues Scalars used to fill the WR2Detailed documentation link
Construct a full MillerIndex with given batch shape. Tensor values are set to the specified value.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalue Value to fill the tensor withDetailed documentation link
Construct a full Quaternion with given batch shape. Tensor values are set to the specified value.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalue Value to fill the tensor withDetailed documentation link
Construct a full R2 with given batch shape. Tensor values are set to the specified value.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalue Value to fill the tensor withDetailed documentation link
Construct a full R3 with given batch shape. Tensor values are set to the specified value.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalue Value to fill the tensor withDetailed documentation link
Construct a full R4 with given batch shape. Tensor values are set to the specified value.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalue Value to fill the tensor withDetailed documentation link
Construct a full Rot with given batch shape. Tensor values are set to the specified value.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalue Value to fill the tensor withDetailed documentation link
Construct a full SFFR4 with given batch shape. Tensor values are set to the specified value.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalue Value to fill the tensor withDetailed documentation link
Construct a full SFR3 with given batch shape. Tensor values are set to the specified value.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalue Value to fill the tensor withDetailed documentation link
Construct a full SR2 with given batch shape. Tensor values are set to the specified value.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalue Value to fill the tensor withDetailed documentation link
Construct a full SSR4 with given batch shape. Tensor values are set to the specified value.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalue Value to fill the tensor withDetailed documentation link
Construct a full SWR4 with given batch shape. Tensor values are set to the specified value.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalue Value to fill the tensor withDetailed documentation link
Construct a full Scalar with given batch shape. Tensor values are set to the specified value.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalue Value to fill the tensor withDetailed documentation link
Construct a full Tensor with given batch shape. Tensor values are set to the specified value.
base_shape Base shapebatch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalue Value to fill the tensor withDetailed documentation link
Construct a full Vec with given batch shape. Tensor values are set to the specified value.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalue Value to fill the tensor withDetailed documentation link
Construct a full WFFR4 with given batch shape. Tensor values are set to the specified value.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalue Value to fill the tensor withDetailed documentation link
Construct a full WR2 with given batch shape. Tensor values are set to the specified value.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalue Value to fill the tensor withDetailed documentation link
Construct a full WSR4 with given batch shape. Tensor values are set to the specified value.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalue Value to fill the tensor withDetailed documentation link
Construct a full WWR4 with given batch shape. Tensor values are set to the specified value.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalue Value to fill the tensor withDetailed documentation link
Construct an identity Tensor with given batch shape.
batch_shape Batch shapen Diagonal size of the identity tensor, i.e., base shape of the identity tensor will be (n,n)shape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimDetailed documentation link
Construct a MillerIndex linearly spaced on the batch/intermediate dimensions. See neml2::dynamic_linspace, neml2::intmd_linspace, or neml2::base_linspace for a detailed explanation.
dim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a Quaternion linearly spaced on the batch/intermediate dimensions. See neml2::dynamic_linspace, neml2::intmd_linspace, or neml2::base_linspace for a detailed explanation.
dim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a R2 linearly spaced on the batch/intermediate dimensions. See neml2::dynamic_linspace, neml2::intmd_linspace, or neml2::base_linspace for a detailed explanation.
dim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a R3 linearly spaced on the batch/intermediate dimensions. See neml2::dynamic_linspace, neml2::intmd_linspace, or neml2::base_linspace for a detailed explanation.
dim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a R4 linearly spaced on the batch/intermediate dimensions. See neml2::dynamic_linspace, neml2::intmd_linspace, or neml2::base_linspace for a detailed explanation.
dim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a Rot linearly spaced on the batch/intermediate dimensions. See neml2::dynamic_linspace, neml2::intmd_linspace, or neml2::base_linspace for a detailed explanation.
dim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a SFFR4 linearly spaced on the batch/intermediate dimensions. See neml2::dynamic_linspace, neml2::intmd_linspace, or neml2::base_linspace for a detailed explanation.
dim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a SFR3 linearly spaced on the batch/intermediate dimensions. See neml2::dynamic_linspace, neml2::intmd_linspace, or neml2::base_linspace for a detailed explanation.
dim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a SR2 linearly spaced on the batch/intermediate dimensions. See neml2::dynamic_linspace, neml2::intmd_linspace, or neml2::base_linspace for a detailed explanation.
dim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a SSR4 linearly spaced on the batch/intermediate dimensions. See neml2::dynamic_linspace, neml2::intmd_linspace, or neml2::base_linspace for a detailed explanation.
dim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a SWR4 linearly spaced on the batch/intermediate dimensions. See neml2::dynamic_linspace, neml2::intmd_linspace, or neml2::base_linspace for a detailed explanation.
dim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a Scalar linearly spaced on the batch/intermediate dimensions. See neml2::dynamic_linspace, neml2::intmd_linspace, or neml2::base_linspace for a detailed explanation.
dim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a Tensor linearly spaced on the batch/intermediate dimensions. See neml2::dynamic_linspace, neml2::intmd_linspace, or neml2::base_linspace for a detailed explanation.
dim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a Vec linearly spaced on the batch/intermediate dimensions. See neml2::dynamic_linspace, neml2::intmd_linspace, or neml2::base_linspace for a detailed explanation.
dim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a WFFR4 linearly spaced on the batch/intermediate dimensions. See neml2::dynamic_linspace, neml2::intmd_linspace, or neml2::base_linspace for a detailed explanation.
dim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a WR2 linearly spaced on the batch/intermediate dimensions. See neml2::dynamic_linspace, neml2::intmd_linspace, or neml2::base_linspace for a detailed explanation.
dim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a WSR4 linearly spaced on the batch/intermediate dimensions. See neml2::dynamic_linspace, neml2::intmd_linspace, or neml2::base_linspace for a detailed explanation.
dim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a WWR4 linearly spaced on the batch/intermediate dimensions. See neml2::dynamic_linspace, neml2::intmd_linspace, or neml2::base_linspace for a detailed explanation.
dim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a MillerIndex logarithmically spaced on the batch dimensions. See neml2::dynamic_logspace, neml2::intmd_logspace, or neml2::base_logspace for a detailed explanation.
base The base of the logarithmdim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a Quaternion logarithmically spaced on the batch dimensions. See neml2::dynamic_logspace, neml2::intmd_logspace, or neml2::base_logspace for a detailed explanation.
base The base of the logarithmdim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a R2 logarithmically spaced on the batch dimensions. See neml2::dynamic_logspace, neml2::intmd_logspace, or neml2::base_logspace for a detailed explanation.
base The base of the logarithmdim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a R3 logarithmically spaced on the batch dimensions. See neml2::dynamic_logspace, neml2::intmd_logspace, or neml2::base_logspace for a detailed explanation.
base The base of the logarithmdim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a R4 logarithmically spaced on the batch dimensions. See neml2::dynamic_logspace, neml2::intmd_logspace, or neml2::base_logspace for a detailed explanation.
base The base of the logarithmdim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a Rot logarithmically spaced on the batch dimensions. See neml2::dynamic_logspace, neml2::intmd_logspace, or neml2::base_logspace for a detailed explanation.
base The base of the logarithmdim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a SFFR4 logarithmically spaced on the batch dimensions. See neml2::dynamic_logspace, neml2::intmd_logspace, or neml2::base_logspace for a detailed explanation.
base The base of the logarithmdim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a SFR3 logarithmically spaced on the batch dimensions. See neml2::dynamic_logspace, neml2::intmd_logspace, or neml2::base_logspace for a detailed explanation.
base The base of the logarithmdim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a SR2 logarithmically spaced on the batch dimensions. See neml2::dynamic_logspace, neml2::intmd_logspace, or neml2::base_logspace for a detailed explanation.
base The base of the logarithmdim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a SSR4 logarithmically spaced on the batch dimensions. See neml2::dynamic_logspace, neml2::intmd_logspace, or neml2::base_logspace for a detailed explanation.
base The base of the logarithmdim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a SWR4 logarithmically spaced on the batch dimensions. See neml2::dynamic_logspace, neml2::intmd_logspace, or neml2::base_logspace for a detailed explanation.
base The base of the logarithmdim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a Scalar logarithmically spaced on the batch dimensions. See neml2::dynamic_logspace, neml2::intmd_logspace, or neml2::base_logspace for a detailed explanation.
base The base of the logarithmdim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a Tensor logarithmically spaced on the batch dimensions. See neml2::dynamic_logspace, neml2::intmd_logspace, or neml2::base_logspace for a detailed explanation.
base The base of the logarithmdim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a Vec logarithmically spaced on the batch dimensions. See neml2::dynamic_logspace, neml2::intmd_logspace, or neml2::base_logspace for a detailed explanation.
base The base of the logarithmdim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a WFFR4 logarithmically spaced on the batch dimensions. See neml2::dynamic_logspace, neml2::intmd_logspace, or neml2::base_logspace for a detailed explanation.
base The base of the logarithmdim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a WR2 logarithmically spaced on the batch dimensions. See neml2::dynamic_logspace, neml2::intmd_logspace, or neml2::base_logspace for a detailed explanation.
base The base of the logarithmdim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a WSR4 logarithmically spaced on the batch dimensions. See neml2::dynamic_logspace, neml2::intmd_logspace, or neml2::base_logspace for a detailed explanation.
base The base of the logarithmdim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a WWR4 logarithmically spaced on the batch dimensions. See neml2::dynamic_logspace, neml2::intmd_logspace, or neml2::base_logspace for a detailed explanation.
base The base of the logarithmdim Where to insert the new dimensionend The ending tensorgroup Dimension group to apply the operation. Options are: intermediate, dynamicnstep The number of steps with even spacing along the new dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstart The starting tensorDetailed documentation link
Construct a MillerIndex from a vector values. The vector will be reshaped according to the specified batch shape.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalues Values in this (flattened) tensorDetailed documentation link
Get the tensor from torch script. The torch script should define a Module with named_buffers that stores the tensor to load. Refer to tests/regression/liquid_infiltration/gold/generate_load_file.py for an example
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimtensor_name Key of named_buffers to extract the tensor from.torch_script Name of the torch script file.Detailed documentation link
Construct a two-dimensional Scalar from a CSV file. A subset of columns can be selected using column_indices or column_names. By default, the CSV is interpreted as column-major, i.e., each column in the CSV corresponds to one row of the 2D Scalar. This behavior can be altered via the indexing option. If batch_shape is specified, an additional reshaping is performed on the 2D Scalar (and the resulting Scalar is not necessarily 2D anymore).
batch_shape Batch shapecolumn_indices Indices of CSV columns.column_names Names of CSV columns.csv_file Path to the CSV filedelimiter Delimiter used to parse the CSV file. Options are SPACE, TAB, SEMICOLON, COMMAindexing Indexing interpretation. Options are ROW_MAJOR, COLUMN_MAJORno_header Whether the CSV file has a header row.shape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimstarting_row Starting row of the CSV file (0-indexed). Rows before this row are ignored. This should be the header row if the CSV file has a header, otherwise it should be the first row of the data. By default the starting row is the 0th row.Detailed documentation link
An orientation, internally defined as a set of Modified Rodrigues parameters given by \( r = n \tan{\frac{\theta}{4}} \) with \( n \) the axis of rotation and \( \theta \) the rotation angle about that axis. However, this class provides a variety of ways to define the orientation in terms of other, more common representations.
angle_convention Euler angle convention, 'Kocks', 'Roe', or 'Bunge'angle_type Type of angles, either 'degrees' or 'radians'input_type The method used to define the angles, 'euler_angles' or 'random'normalize If true do a shadow parameter replacement of the underlying MRP representation to move the inputs farther away from the singularityquantity Number (batch size) of random orientationsshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalues Input Euler angles, as a flattened n-by-3 matrixDetailed documentation link
Construct a Quaternion from a vector values. The vector will be reshaped according to the specified batch shape.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalues Values in this (flattened) tensorDetailed documentation link
Get the tensor from torch script. The torch script should define a Module with named_buffers that stores the tensor to load. Refer to tests/regression/liquid_infiltration/gold/generate_load_file.py for an example
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimtensor_name Key of named_buffers to extract the tensor from.torch_script Name of the torch script file.Detailed documentation link
Construct a R2 from a vector values. The vector will be reshaped according to the specified batch shape.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalues Values in this (flattened) tensorDetailed documentation link
Get the tensor from torch script. The torch script should define a Module with named_buffers that stores the tensor to load. Refer to tests/regression/liquid_infiltration/gold/generate_load_file.py for an example
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimtensor_name Key of named_buffers to extract the tensor from.torch_script Name of the torch script file.Detailed documentation link
Construct a R3 from a vector values. The vector will be reshaped according to the specified batch shape.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalues Values in this (flattened) tensorDetailed documentation link
Get the tensor from torch script. The torch script should define a Module with named_buffers that stores the tensor to load. Refer to tests/regression/liquid_infiltration/gold/generate_load_file.py for an example
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimtensor_name Key of named_buffers to extract the tensor from.torch_script Name of the torch script file.Detailed documentation link
Construct a R4 from a vector values. The vector will be reshaped according to the specified batch shape.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalues Values in this (flattened) tensorDetailed documentation link
Get the tensor from torch script. The torch script should define a Module with named_buffers that stores the tensor to load. Refer to tests/regression/liquid_infiltration/gold/generate_load_file.py for an example
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimtensor_name Key of named_buffers to extract the tensor from.torch_script Name of the torch script file.Detailed documentation link
Construct a random MillerIndex with given batch shape. Tensor values are set to random values. Values are drawn uniformly from the sample space.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionmax Maximum random value.min Minimum random value.shape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimDetailed documentation link
Construct a random Quaternion with given batch shape. Tensor values are set to random values. Values are drawn uniformly from the sample space.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionmax Maximum random value.min Minimum random value.shape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimDetailed documentation link
Construct a random R2 with given batch shape. Tensor values are set to random values. Values are drawn uniformly from the sample space.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionmax Maximum random value.min Minimum random value.shape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimDetailed documentation link
Construct a random R3 with given batch shape. Tensor values are set to random values. Values are drawn uniformly from the sample space.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionmax Maximum random value.min Minimum random value.shape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimDetailed documentation link
Construct a random R4 with given batch shape. Tensor values are set to random values. Values are drawn uniformly from the sample space.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionmax Maximum random value.min Minimum random value.shape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimDetailed documentation link
Construct a random Rot with given batch shape. Tensor values are set to random values. Values are drawn uniformly from the sample space.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimDetailed documentation link
Construct a random SFFR4 with given batch shape. Tensor values are set to random values. Values are drawn uniformly from the sample space.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionmax Maximum random value.min Minimum random value.shape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimDetailed documentation link
Construct a random SFR3 with given batch shape. Tensor values are set to random values. Values are drawn uniformly from the sample space.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionmax Maximum random value.min Minimum random value.shape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimDetailed documentation link
Construct a random SR2 with given batch shape. Tensor values are set to random values. Values are drawn uniformly from the sample space.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionmax Maximum random value.min Minimum random value.shape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimDetailed documentation link
Construct a random SSR4 with given batch shape. Tensor values are set to random values. Values are drawn uniformly from the sample space.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionmax Maximum random value.min Minimum random value.shape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimDetailed documentation link
Construct a random SWR4 with given batch shape. Tensor values are set to random values. Values are drawn uniformly from the sample space.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionmax Maximum random value.min Minimum random value.shape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimDetailed documentation link
Construct a random Scalar with given batch shape. Tensor values are set to random values. Values are drawn uniformly from the sample space.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionmax Maximum random value.min Minimum random value.shape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimDetailed documentation link
Construct a random Tensor with given batch shape. Tensor values are set to random values. Values are drawn uniformly from the sample space.
base_shape Base shapebatch_shape Batch shapeintermediate_dimension Intermediate dimensionmax Maximum random value.min Minimum random value.shape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimDetailed documentation link
Construct a random Vec with given batch shape. Tensor values are set to random values. Values are drawn uniformly from the sample space.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionmax Maximum random value.min Minimum random value.shape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimDetailed documentation link
Construct a random WFFR4 with given batch shape. Tensor values are set to random values. Values are drawn uniformly from the sample space.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionmax Maximum random value.min Minimum random value.shape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimDetailed documentation link
Construct a random WR2 with given batch shape. Tensor values are set to random values. Values are drawn uniformly from the sample space.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionmax Maximum random value.min Minimum random value.shape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimDetailed documentation link
Construct a random WSR4 with given batch shape. Tensor values are set to random values. Values are drawn uniformly from the sample space.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionmax Maximum random value.min Minimum random value.shape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimDetailed documentation link
Construct a random WWR4 with given batch shape. Tensor values are set to random values. Values are drawn uniformly from the sample space.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionmax Maximum random value.min Minimum random value.shape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimDetailed documentation link
Construct a Rot from a vector values. The vector will be reshaped according to the specified batch shape.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalues Values in this (flattened) tensorDetailed documentation link
Get the tensor from torch script. The torch script should define a Module with named_buffers that stores the tensor to load. Refer to tests/regression/liquid_infiltration/gold/generate_load_file.py for an example
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimtensor_name Key of named_buffers to extract the tensor from.torch_script Name of the torch script file.Detailed documentation link
Construct a SFFR4 from a vector values. The vector will be reshaped according to the specified batch shape.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalues Values in this (flattened) tensorDetailed documentation link
Get the tensor from torch script. The torch script should define a Module with named_buffers that stores the tensor to load. Refer to tests/regression/liquid_infiltration/gold/generate_load_file.py for an example
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimtensor_name Key of named_buffers to extract the tensor from.torch_script Name of the torch script file.Detailed documentation link
Construct a SFR3 from a vector values. The vector will be reshaped according to the specified batch shape.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalues Values in this (flattened) tensorDetailed documentation link
Get the tensor from torch script. The torch script should define a Module with named_buffers that stores the tensor to load. Refer to tests/regression/liquid_infiltration/gold/generate_load_file.py for an example
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimtensor_name Key of named_buffers to extract the tensor from.torch_script Name of the torch script file.Detailed documentation link
Construct a SR2 from a vector values. The vector will be reshaped according to the specified batch shape.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalues Values in this (flattened) tensorDetailed documentation link
Get the tensor from torch script. The torch script should define a Module with named_buffers that stores the tensor to load. Refer to tests/regression/liquid_infiltration/gold/generate_load_file.py for an example
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimtensor_name Key of named_buffers to extract the tensor from.torch_script Name of the torch script file.Detailed documentation link
Construct a SSR4 from a vector values. The vector will be reshaped according to the specified batch shape.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalues Values in this (flattened) tensorDetailed documentation link
Get the tensor from torch script. The torch script should define a Module with named_buffers that stores the tensor to load. Refer to tests/regression/liquid_infiltration/gold/generate_load_file.py for an example
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimtensor_name Key of named_buffers to extract the tensor from.torch_script Name of the torch script file.Detailed documentation link
Construct a SWR4 from a vector values. The vector will be reshaped according to the specified batch shape.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalues Values in this (flattened) tensorDetailed documentation link
Get the tensor from torch script. The torch script should define a Module with named_buffers that stores the tensor to load. Refer to tests/regression/liquid_infiltration/gold/generate_load_file.py for an example
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimtensor_name Key of named_buffers to extract the tensor from.torch_script Name of the torch script file.Detailed documentation link
Construct a Scalar from a vector values. The vector will be reshaped according to the specified batch shape.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalues Values in this (flattened) tensorDetailed documentation link
Get the tensor from torch script. The torch script should define a Module with named_buffers that stores the tensor to load. Refer to tests/regression/liquid_infiltration/gold/generate_load_file.py for an example
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimtensor_name Key of named_buffers to extract the tensor from.torch_script Name of the torch script file.Detailed documentation link
Returns a tensor of symmetry operations for a given symmetr group represented in orbifold notation.
orbifold A string giving the orbifold representation of the group, for example 432 for the typical cubic crystal system defined by chiral octahedral symmetryshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimDetailed documentation link
Construct a Tensor from a vector values. The vector will be reshaped according to the specified batch shape.
base_shape Base shapebatch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalues Values in this (flattened) tensorDetailed documentation link
Construct a Vec from a vector values. The vector will be reshaped according to the specified batch shape.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalues Values in this (flattened) tensorDetailed documentation link
Get the tensor from torch script. The torch script should define a Module with named_buffers that stores the tensor to load. Refer to tests/regression/liquid_infiltration/gold/generate_load_file.py for an example
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimtensor_name Key of named_buffers to extract the tensor from.torch_script Name of the torch script file.Detailed documentation link
Construct a WFFR4 from a vector values. The vector will be reshaped according to the specified batch shape.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalues Values in this (flattened) tensorDetailed documentation link
Get the tensor from torch script. The torch script should define a Module with named_buffers that stores the tensor to load. Refer to tests/regression/liquid_infiltration/gold/generate_load_file.py for an example
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimtensor_name Key of named_buffers to extract the tensor from.torch_script Name of the torch script file.Detailed documentation link
Construct a WR2 from a vector values. The vector will be reshaped according to the specified batch shape.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalues Values in this (flattened) tensorDetailed documentation link
Get the tensor from torch script. The torch script should define a Module with named_buffers that stores the tensor to load. Refer to tests/regression/liquid_infiltration/gold/generate_load_file.py for an example
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimtensor_name Key of named_buffers to extract the tensor from.torch_script Name of the torch script file.Detailed documentation link
Construct a WSR4 from a vector values. The vector will be reshaped according to the specified batch shape.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalues Values in this (flattened) tensorDetailed documentation link
Get the tensor from torch script. The torch script should define a Module with named_buffers that stores the tensor to load. Refer to tests/regression/liquid_infiltration/gold/generate_load_file.py for an example
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimtensor_name Key of named_buffers to extract the tensor from.torch_script Name of the torch script file.Detailed documentation link
Construct a WWR4 from a vector values. The vector will be reshaped according to the specified batch shape.
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimvalues Values in this (flattened) tensorDetailed documentation link
Get the tensor from torch script. The torch script should define a Module with named_buffers that stores the tensor to load. Refer to tests/regression/liquid_infiltration/gold/generate_load_file.py for an example
batch_shape Batch shapeintermediate_dimension Intermediate dimensionshape_manipulation_args A list of arguments corresponding to each shape manipulation operation. The number of entries should match the number of operations in 'shape_manipulations'. Each entry is a tensor shape that encodes the arguments for the corresponding operation. For operations that do not require any argument, an empty shape, i.e. (), should be used.shape_manipulations A list of shape manipulation operations to apply to the created tensor. Supported operations are: intmd_flatten, dynamic_expand, dynamic_reshape, dynamic_squeeze, intmd_expand, dynamic_transpose, dynamic_flatten, intmd_reshape, intmd_squeeze, INVALID, intmd_unsqueeze, dynamic_movedim, intmd_transpose, dynamic_unsqueeze, intmd_movedimtensor_name Key of named_buffers to extract the tensor from.torch_script Name of the torch script file.Detailed documentation link
1.12.0