.round()torch.einsum('bij,ki->bij'）

This code is not complete and cannot be executed.

Assuming that the missing part of the code is the input tensor and that it is a 3-dimensional tensor of shape (batch_size, input_size, input_size), the code would round the tensor elements to the nearest integer using the round() function and then perform an element-wise multiplication of the tensor with a 2-dimensional tensor of shape (input_size, output_size) using the einsum() function.

The resulting tensor would have the same shape as the input tensor, with each element being the product of the corresponding element in the input tensor and the corresponding element in the 2-dimensional tensor.

Here is an example of how the code could look like:

import torch

# create input tensor
batch_size = 2
input_size = 3
input_tensor = torch.randn(batch_size, input_size, input_size)

# create 2-dimensional tensor
output_size = 4
tensor_2d = torch.randn(input_size, output_size)

# round input tensor elements to nearest integer
rounded_tensor = input_tensor.round()

# perform element-wise multiplication using einsum
result_tensor = torch.einsum('bij,ki->bij', rounded_tensor, tensor_2d)

print(result_tensor.shape)  # output: torch.Size([2, 3, 4])