dtype issue fix for insertion and additional checks

[McHaillet]

list of changes:

• added a valuerror check in case values and image for insertion do not have the same dtype
• added explicit dtype for weights allocation in linear insertion
• convert weight to data dtype in insertion to prevent upcasting but allow flexible weights -- this allows running with a weight tensor of float64 while the data dtype is complex64, makes input to the function the least error prone I think
• added tests to check for dtype and ensure valueerror is raised

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[McHaillet]

@alisterburt I am only a bit uncertain about the type casting of the w tensor -- maybe not the neatest solution but we can always adapt later. If the tensor is already correct type thought it just returns self.