I tried updating the both ArviZ and PyMC but I am still getting the following error

ValueError: Input dimension mis-match. (input[3].shape[0] = 1860, input[4].shape[0] = 900)

Apply node that caused the error: Elemwise{Composite{Switch(i0, (i1 * ((-(i2 * sqr((i3 - ((i4 * i5) + i6 + (i7 * i8) + (i9 * i8) + i10 + (i11 * i12) + i13 + i14 + (i15 * i16) + (i17 * i18 * i8)))))) + i19)), i20)}}[(0, 4)](Elemwise{Composite{Cast{int8}(GT(i0, i1))}}.0, TensorConstant{(1,) of 0.5}, Elemwise{Composite{inv(sqr(i0))}}[(0, 0)].0, TensorConstant{[3.3082953…7540281 ]}, AdvancedSubtensor1.0, <TensorType(float64, vector)>, AdvancedSubtensor1.0, AdvancedSubtensor1.0, <TensorType(float64, vector)>, AdvancedSubtensor1.0, AdvancedSubtensor1.0, InplaceDimShuffle{x}.0, <TensorType(float64, vector)>, AdvancedSubtensor1.0, AdvancedSubtensor1.0, InplaceDimShuffle{x}.0, <TensorType(float64, vector)>, AdvancedSubtensor1.0, <TensorType(float64, vector)>, Elemwise{Composite{log((i0 * i1))}}.0, TensorConstant{(1,) of -inf})

Toposort index: 15

Inputs types: [TensorType(int8, (True,)), TensorType(float64, (True,)), TensorType(float64, (True,)), TensorType(float64, vector), TensorType(float64, vector), TensorType(float64, vector), TensorType(float64, vector), TensorType(float64, vector), TensorType(float64, vector), TensorType(float64, vector), TensorType(float64, vector), TensorType(float64, (True,)), TensorType(float64, vector), TensorType(float64, vector), TensorType(float64, vector), TensorType(float64, (True,)), TensorType(float64, vector), TensorType(float64, vector), TensorType(float64, vector), TensorType(float64, (True,)), TensorType(float32, (True,))]

Inputs shapes: [(1,), (1,), (1,), (1860,), (900,), (900,), (900,), (900,), (900,), (900,), (900,), (1,), (900,), (900,), (900,), (1,), (900,), (900,), (900,), (1,), (1,)]

Inputs strides: [(1,), (8,), (8,), (8,), (8,), (8,), (8,), (8,), (8,), (8,), (8,), (8,), (8,), (8,), (8,), (8,), (8,), (8,), (8,), (8,), (4,)]

Inputs values: [array([1], dtype=int8), array([0.5]), array([2.19361905]), ‘not shown’, ‘not shown’, ‘not shown’, ‘not shown’, ‘not shown’, ‘not shown’, ‘not shown’, ‘not shown’, array([0.10929279]), ‘not shown’, ‘not shown’, ‘not shown’, array([0.2955149]), ‘not shown’, ‘not shown’, ‘not shown’, array([-1.05232435]), array([-inf], dtype=float32)]

Outputs clients: [[‘output’]]

HINT: Re-running with most Theano optimization disabled could give you a back-trace of when this node was created. This can be done with by setting the Theano flag ‘optimizer=fast_compile’. If that does not work, Theano optimizations can be disabled with ‘optimizer=None’.

HINT: Use the Theano flag ‘exception_verbosity=high’ for a debugprint and storage map footprint of this apply node.