method of moments geometric distribution

Torch.rand() torch.rand_like() torch.randn() torch.randn_like() torch.randint() torch.randint_like() torch.randperm() Random sampling creation ops are listed under Random sampling and Returns the total number of elements in the input tensor.ĭisables denormal floating numbers on CPU. Sets the default torch.Tensor type to floating point tensor type t. Sets the default torch.Tensor to be allocated on device. Get the current default floating point torch.dtype. Sets the default floating point dtype to d. Returns True if the input is a single element tensor which is not equal to zero after type conversions. Returns True if the data type of input is a floating point data type i.e., one of torch.float64, torch.float32, torch.float16, and torch.bfloat16. Returns True if the input is a conjugated tensor, i.e. Returns True if the data type of input is a complex data type i.e., one of ple圆4, and plex128. Returns True if obj is a PyTorch storage object. On an NVIDIA GPU with compute capability >= 3.0. It has a CUDA counterpart, that enables you to run your tensor computations Tensors and arbitrary types, and other useful utilities. Tensors and defines mathematical operations over these tensors.Īdditionally, it provides many utilities for efficient serialization of The torch package contains data structures for multi-dimensional Extending torch.func with autograd.Function.CPU threading and TorchScript inference.CUDA Automatic Mixed Precision examples.

Method of moments geometric distribution