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`dim` (optional): For arrays of rank > 2, shall be an integer array of size 2 specifying the dimensions over which to compute the matrix norm. Default value is `[1,2]`. It is an `intent(in)` argument.
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`err` (optional): Shall be a `type(linalg_state_type)` value. This is an `intent(out)` argument.
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### Return value
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For rank-2 input arrays, the return value `x` is a scalar containing the matrix norm.
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For arrays of rank > 2, if the optional `dim` argument is present, `x` is a rank `n-2` array with the same shape as \( A \) except
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for dimensions `dim(1)` and `dim(2)`, which are dropped. Each element of `x` contains the matrix norm of the corresponding submatrix of \( A \),
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evaluated over the specified dimensions only, with the given order.
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If an invalid norm type is provided, defaults to 1-norm and raises `LINALG_ERROR`.
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Raises `LINALG_VALUE_ERROR` if any of the arguments has an invalid size.
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If `err` is not present, exceptions trigger an `error stop`.
For a complex varible \( z=(x + y i) \) with independent real \( x \) and imaginary \( y \) parts, the joint probability density function is the product of the the corresponding real and imaginary marginal pdfs:[^2]
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$$f(x + y \mathit{i}) = f(x) f(y) = \frac{1}{2\sigma_{x}\sigma_{y}} \exp{\left[-\frac{1}{2}\left(\left(\frac{x-\mu_x}{\sigma_{x}}\right)^{2}+\left(\frac{y-\mu_y}{\sigma_{y}}\right)^{2}\right)\right]}$$
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$$f(x + y \mathit{i}) = f(x) f(y) = \frac{1}{2\pi\sigma_{x}\sigma_{y}} \exp{\left[-\frac{1}{2}\left(\left(\frac{x-\mu_x}{\sigma_{x}}\right)^{2}+\left(\frac{y-\mu_y}{\sigma_{y}}\right)^{2}\right)\right]}$$
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