Tensor mismatch on transpose 2x4 using bfloat16 data format

[dgolubovicTT]

Running single op transpose test for tensor of dimensions 2x4 and bfloat16 data format(transpose dims: dim0=-1 , dim1=-2) causes tensor mismatch.
However, this can't be reproed on ttnn transpose test.
Here is the ttnn IR of transpose case causing tensor mismatch: test_transpose_2x4_mismatch_ttnn.txt

Here is the ttnn repro test that passes:

@pytest.mark.parametrize("b", [1])
@pytest.mark.parametrize("h", [2])
@pytest.mark.parametrize("w", [4])
@pytest.mark.parametrize("dim0", [-1])
@pytest.mark.parametrize("dim1", [-2])
def test_transpose(device, b, h, w, dim0, dim1):
    torch.manual_seed(1234)

    torch_input_tensor = torch_random((1, b, h, w), -0.1, 0.1, dtype=torch.bfloat16)
    torch_output_tensor = torch_input_tensor.transpose(dim0, dim1)

    input_tensor = ttnn.to_device(ttnn.from_torch(torch_input_tensor), device, memory_config=ttnn.DRAM_MEMORY_CONFIG)
    input_tensor = ttnn.to_layout(input_tensor, layout=ttnn.TILE_LAYOUT)
    output_tensor = ttnn.transpose(input_tensor, dim0, dim1, memory_config=ttnn.DRAM_MEMORY_CONFIG)
    output_tensor = ttnn.from_device(output_tensor)
    output_tensor = ttnn.to_layout(output_tensor, layout=ttnn.ROW_MAJOR_LAYOUT)
    output_tensor = ttnn.to_torch(output_tensor)
    print("torch_input_tensor: ")
    print(torch_input_tensor)
    print("output_tensor: ")
    print(output_tensor)
    print("torch_output_tensor: ")
    print(torch_output_tensor)

    assert_with_pcc(torch_output_tensor, output_tensor)

Comparing ttnn test and ttnn IR I can't find what is the difference that could cause ttnn test to pass and ttnn IR to fail.
Need help from someone on mlir side @sdjordjevicTT.

If you need anything else feel free to ask.