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In this problem, we are given a natural number N. Our task is to find the sum of divisors of all the divisors of a natural number.Let's take an example to understand the problem, Input : N = 12 Output : 55Explanation −The divisors of 12 are 1, 2, 3, 4, 6, 12 Sum of divisors = (1) + (1 + 2) + (1 + 3) + (1 + 2 + 4) + (1 + 2 + 3 + 6) + (1 + 2 + 3 + 4 + 6 + 12) = 1 + 3 + 4 + 7 ... Read More
To find the indices of the maximum value of the elements in an input tensor, we can apply the torch.argmax() function. It returns the indices only, not the element value. If the input tensor has multiple maximal values, then the function will return the index of the first maximal element. We can apply the torch.argmax() function to compute the indices of the maximum values of a tensor across a dimension..Syntaxtorch.argmax(input)StepsWe could use the following steps to find the indices of the maximum values of all elements in the input tensor −Import the required library. In all the following examples, the ... Read More
To compute elementwise logical AND of given input tensors we apply torch.logical_and(). It takes two input tensors and computes the logical AND element wise. The zeros in the tensors are treated as False and non-zeros as True. The input tensors may be of any dimension.The torch.logical_or() function computes elementwise logical OR of the given input tensors. It also takes two input tensors and outputs a tensor with True or False. As same in logical AND zeros are treated as False and non-zeros are treated as True.The input tensors may be of any dimension.To compute the elementwise NOT of a given ... Read More
To estimate the gradient of a function, we can apply the torch.gradient() function. This function estimates the gradient using the second-order accurate central differences method. We can estimate the gradient in one or more dimensions. The function of which the gradient is to be estimated may be defined on a real or complex domain. In the process of estimating the gradients, the gradient is estimated by estimating each partial derivative of the function independently.Syntaxtorch.gradient(values)where the parameter values is the tensor that represents the values of the function.StepsWe could use the following steps to estimate the gradient of a function −Import ... Read More
The torch.asinh() method computes the inverse hyperbolic sine of each element of the input tensor. It supports both real and complex-valued inputs. It supports any dimension of the input tensor.Syntaxtorch.asinh(input)where input is the input tensor.OutputIt returns a tensor inverse hyperbolic sine of each element.StepsTo compute the inverse hyperbolic sine of each element in the input tensor, you could follow the steps given below −Import the required library. In all the following examples, the required Python library is torch. Make sure you have already installed it.import torchCreate a torch tensor and print it.input = torch.randn(3, 4) print("Input Tensor:", input)Compute the inverse ... Read More
The torch.rsqrt() method computes the reciprocal of square-root of each element of the input tensor. It supports both real and complex-valued inputs. If an element in the input tensor is zero, then the corresponding element in the output tensor is NaN.Syntaxtorch.rsqrt(input)Parametersinput – Input tensorOutputIt returns a tensor with reciprocal of square-root.StepsImport the required library. In all the following examples, the required Python library is torch. Make sure you have already installed it.import torchCreate a torch tensor and print it.input = torch.randn(3, 4) print("Input Tensor:", input)Compute the reciprocal of the square-root of each element in the input tensor using torch.rsqrt(input). Here ... Read More
To compute the elementwise angle of the given input tensor, we apply torch.angle(). It takes an input tensor and returns a tensor with angle in radian computed element wise. To convert the angles into the degree we multiply the angle in radian by 180/np.pi. It supports both real and complex-valued tensors.Syntaxtorch.angle(input)StepsTo compute the elementwise angle, you could follow the steps given below −Import the required library. In all the following examples, the required Python library is torch. Make sure you have already installed it.import torchDefine torch tensors and print them.input = torch.tensor([1 + 1j, -1 -4j, 3-2j])Compute torch.angle(input). It is ... Read More
To compute bitwise AND of given input tensors we apply torch.bitwise_and(). The input tensors must be of integral or Boolean types. For bool tensors, it computes the logical AND.To compute bitwise NOT of a given input tensor we apply torch.bitwise_not() method. The input tensors must be of integral or Boolean types. For bool tensors, it computes the logical OR.To compute the bitwise NOT of a given input tensor we apply torch.bitwise_not() method. The input tensor must be of integral or Boolean types. For bool tensors, it computes the logical NOT.Syntaxtorch.bitwise_and(input1, input2) torch.bitwise_or(input1, input2) torch.bitwise_not(input)StepsImport the required library. In all the ... Read More
The torch.acos() method computes the inverse cosine of each element of an input tensor. It supports both real and complex-valued inputs. It supports any dimension of the input tensor. The elements of the input tensor must be in the range [-1, 1], as the inverse cosine function has its domain as [-1, 1].The torch.acosh() method computes the inverse hyperbolic cosine of each element of the input tensor. It also supports both real and complex-valued inputs of any dimension. The elements of the input tensor must be any number greater or equal to 1, as the inverse cosine function has its ... Read More
To create a tensor whose elements are sampled from a Poisson distribution, we apply the torch.poisson() method. This method takes a tensor whose elements are rate parameters as input tensor. It returns a tensor whose elements are sampled from a Poisson distribution with the rate parameter.Syntaxtorch.poisson(rates)where the parameter rates is a torch tensor of rate parameters. Rate parameters are used to sample elements from a Poisson distribution.StepsWe could use the following steps to create a tensor whose elements are sampled from a Poisson distribution −Import the required library. In all the following examples, the required Python library is torch. Make ... Read More