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#CREATE A MATRIX IN G DOCS HOW TO#

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#CREATE A MATRIX IN G DOCS HOW TO#

To continue learning about how to extend Google Docs with Apps Script, take aĮxcept as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. Since this is an example add-on, our tutorial ends here. Select it and click the blue Translate button. To test it, type some text into your document, then Read the notice carefully, then click Allow. A second dialog box then requests authorization for

A dialog box appears and tell you that the script requires authorization.Ĭlick Continue.

(If you chose a different name for your script, that nameĪppears instead.) Click Add-ons > Translate Quickstart > Start.

After a few seconds, a Translate Quickstart sub-menu will appear under theĪdd-ons menu.

Switch back to your document and reload the page.

Several places, including the authorization dialog. The script's name is shown to end users in

Click Untitled project, name your script "Translate.

* element The element after which to display the error. * This method is only used by the regular add-on, and is never called by * Creates a menu entry in the Google Docs UI when the document is opened.

* presented to users will reflect this limited scope. * attempt to read or modify the files in which the add-on is used, Returns a copy of this object in CUDA memory.* The above comment directs Apps Script to limit the scope of file Returns a copy of this object in CPU memory. Returns a contiguous in memory tensor containing the same data as self tensor.Ĭopies the elements from src into self tensor and returns self. Self.char() is equivalent to self.to(torch.int8). te() is equivalent to self.to(torch.uint8).įills the tensor with numbers drawn from the Cauchy distribution: Self.bool() is equivalent to self.to(torch.bool). In-place version of bitwise_right_shift() Self.bfloat16() is equivalent to self.to(torch.bfloat16). Returns a result tensor where each result \texttt) Bernoulli ( p ). In-place version of absolute() Alias for abs_()Īpplies the function callable to each element in the tensor, replacing each element with the value returned by callable.Ĭomputes the gradient of current tensor w.r.t. Returns a new tensor containing imaginary values of the self tensor. Returns a new tensor containing real values of the self tensor for a complex-valued input tensor. This attribute is None by default and becomes a Tensor the first time a call to backward() computes gradients for self. Is True if the Tensor is a meta tensor, False otherwise. Is True if the Tensor is quantized, False otherwise. Is True if the Tensor is stored on the GPU, False otherwise. Returns a Tensor of size size filled with 0. Returns a Tensor of size size filled with 1. Returns a Tensor of size size filled with uninitialized data. Returns a Tensor of size size filled with fill_value. Returns a new Tensor with data as the tensor data. mH ¶Īccessing this property is equivalent to calling adjoint(). X.mT is equivalent to x.transpose(-2, -1). Returns a view of this tensor with the last two dimensions transposed. MH: An attribute that also works on batches of matrices. Torch.Tensor is an alias for the default tensor type ( torch.FloatTensor). Currently it’s only supported in EmbeddingBag operator. Quantized 4-bit integer is stored as a 8-bit signed integer. Useful when range is important, since it has the same Sometimes referred to as Brain Floating Point: uses 1 sign, 8 exponent, and 7 Useful when precision is important at the expense of range. Various constructors create Matrices from two dimensional arrays of double precision floating point numbers. Sometimes referred to as binary16: uses 1 sign, 5 exponent, and 10 Torch defines 10 tensor types with CPU and GPU variants which are as follows: CPU threading and TorchScript inference.CUDA Automatic Mixed Precision examples.