torch.lstsq用法
    ## Usage of `torch.lstsq`。
    `torch.lstsq` function in PyTorch is used to compute the least squares solution of a linear matrix equation. It can solve systems of linear equations where the number of equations is larger or equal to the number of variables.
    The general form of a linear matrix equation is:
    Ax = b.
    where `A` is an `m x n` matrix, `x` is an `n x 1` vector of unknowns, and `b` is an `m x 1` vector of observations.
    `torch.lstsq` function takes as input a tensor `A` of shape `(m, n)` and a tensor `b` of shape `(m, k)`, where `k` is the number of solutions to be computed. It returns a tensor `x` of shape `(n, k)` containing the least squares solutions to the system of equations.
    The function has the following syntax:
    python.
    torch.lstsq(A, b, driver='gels')。
    where:
    `A` Input tensor of shape `(m, n)` representing the coefficient matrix.
    `b` Input tensor of shape `(m, k)` representing the observations.
    `driver` (optional) String specifying the linear solver to use. Options are "gels" (default) and "gelsy".
    The following code shows an example of how to use `torch.lstsq` to solve a system of linear equations:
    python.
    import torch.
    A = torch.randn(10, 5)。
    b = torch.randn(10, 3)。sort of torch翻译
    # Compute the least squares solution.
    x, residuals, rank, singular_values = torch.lstsq(A, b)。
    # Print the solution.
    print(x)。
    ## 中文回答：
    torch.lstsq 的用法。
    PyTorch 中的 `torch.lstsq` 函数用于计算线性矩阵方程的最小二乘解。它可以求解方程组中方程数大于或等于变量数的情况。
    线性矩阵方程的一般形式为：
    Ax = b.
    其中 `A` 是 `m x n` 矩阵，`x` 是 `n x 1` 未知量向量，`b` 是 `m x 1` 观测值向量。
    `torch.lstsq` 函数的输入是形状为 `(m, n)` 的张量 `A` 和形状为 `(m, k)` 的张量 `b`，其中 `k` 是要计算的解的数量。它返回形状为 `(n, k)` 的张量 `x`，其中包含方程组的最小二乘解。

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