argmin | Mathematical optimization in pure Rust | Computer Vision library

I am training a VQVAE with this dataset (64x64x3). I have downloaded it locally and loaded it with keras in Jupyter notebook. The problem is that when I ran fit() to train the model I get this error: ValueError: Layer "vq_vae" expects 1 input(s), but it received 2 input tensors. Inputs received: [, ] . I have taken most of the code from here and adapted it myself. But for some reason I can't make it work for other datasets. You can ignore most of the code here and check it in the page, help is much appreciated.

The code I have so far:

I'm looking to slice out the minimum value along the first axis of an array. For example, in the code below, I want to print out np.array([13, 0, 12, 3]). However, the slicing isn't behaving as I would think it does.
(I do need the argmin array later and don't want to just use np.min(g, axis=1))

I am trying to build a histogram of my dataset online while the samples "x" are being generated so that I can use this histogram to stir the direction of the distribution of samples "y". Here is a toy example which is not really working:

I use the following code to plot a scatter plot. I have been trying to plot the lower boundary for it. I tried following the other question but I was unable to replicate it for my objective function and data. The code is as follows :

I have to compute the Aroon indicator on data stored in a dataframe:

Let us have a numpy array (float) with shape equal to (36, 2, 400, 400). Let us say the 400 by 400 represents an image. Then for each pixel I would like to find the two values (second dimension) which are when taking the norm over the second dimension, the lowest with respect to the first dimension. So I end up with an array of shape (2, 400, 400).

With np.argmin(np.linalg.norm(array, axis=1), axis=0) I am able to get the index for each of those 2 by 400 by 400 pixels which is almost what I want. But now I want to use this number to slice the original array in the first dimension so I am left with an array of shape (2, 400, 400).

What I can do is loop over all indices and construct the result pixel by pixel, but I am convinced there is a smarter way. Can anyone help me with a smarter way?

A minimal reproducible example as requested where distances is the array:

Given the following matrix:

I was using pyspark on AWS EMR (4 r5.xlarge as 4 workers, each has one executor and 4 cores), and I got AttributeError: Can't get attribute 'new_block' on . Below is a snippet of the code that threw this error:

The present code selects minimum values by scanning the adjoining elements in the same and the succeeding row. However, I want the code to select all the values if they are less than the threshold value. For example, in row 2, I want the code to pick both 0.86 and 0.88 since both are less than 0.9, and not merely minimum amongst 0.86,0.88. Basically, the code should pick up the minimum value if all the adjoining elements are greater than the threshold. If that's not the case, it should pick all the values less than the threshold.