Something seems strange about the data loading vs. the weights here. If the weights to the first layer have 32 input channels in_channels=w, then the input should also have 32 channels. However, it looks like the 32 in the input is actually the “batch” dimension, with the channel dimension missing (as 100 is the sequence length dimension).
You can try reducing the number of input channels for the convolution to just 1, and reshaping the input to [32, 1, 100] in this case, or change the construction of the TimeseriesDataset so the input has two channels to match the first convolution layer.
You can read the details of the expected input and output shapes for a 1d convolution here 3. Note that the input needs to be three dimensional, and match the number of input channels to the convolution.