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

To compute the Heaviside step function for each element in the input tensor, we use the torch.heaviside() method. It accepts two parameters − input and values. It returns a new tensor with a computed heaviside step function.The value of heaviside function is the same as values if input=0. The value of heaviside is zero if input is less than zero. The value of heaviside is 1 if input is greater than zero. It accepts torch tensors of any dimension. It is also called the unit step function.Syntaxtorch.heaviside(input, values)StepsWe could use the following steps to compute the Heaviside step function −Import ... Read More

To draw binary random numbers (0 or 1) from a Bernoulli distribution, we apply the torch.bernoulli() method. The input to this method is a torch tensor containing the probabilities of drawing 1. These probabilities are used to draw binary random numbers (0 or 1).As the input tensor contains the probabilities, all the elements should be in the range [0, 1]. It returns a tensor whose elements (0 or 1) are randomly selected from a Bernoulli distribution with the input probabilities.Syntaxtorch.bernoulli(input)where, the parameter input is a torch tensor containing the probabilities of drawing 1. These probabilities are used to draw the ... Read More

To create a tensor of random numbers drawn from separate normal distributions whose mean and std are given, we apply the torch.normal() method. This method takes two input parameters − mean and std.mean is a tensor with the mean of each output element’s normal distribution, andstd is a tensor with the standard deviation of each output element’s normal distribution.It returns a tensor of random numbers drawn from separate normal distributions whose mean and standard deviation are mean and std.Syntaxtorch.normal(mean, std)StepsWe could use the following steps to create a tensor of random numbers drawn from separate normal distributions −Import the required ... Read More