[TVM] Decompose repeat_interleave pytorch op and add sanity test

(cherry picked from commit a4ff19a91355338f49d068d9e64302c52c258825)

pybuda/test/tvm/sanity/tests_B/test_pattern_matcher.py

@@ -193,3 +193,57 @@ def forward(self,input):
             verify_tvm_compile=True,
         )
     )
+
+@pytest.mark.parametrize("input_shape", ((1, 4), (1, 4, 3), (1, 2, 7, 6)))
+@pytest.mark.parametrize("repeat_dims", (1, 2, 3, -1, -2, -3))
+@pytest.mark.parametrize("num_repeats", (2, 3))
+def test_repeat_interleave_pytorch(test_device, input_shape, repeat_dims, num_repeats):
+
+    dims = repeat_dims
+    if dims < 0:
+        dims = len(input_shape) + dims
+        if dims < 0:
+            pytest.skip()
+
+    if dims > int(len(input_shape) - 1) or input_shape[dims] == 1:
+        pytest.skip()
+
+    # Set PyBuda configuration parameters
+    compiler_cfg = pybuda.config._get_global_compiler_config()
+    compiler_cfg.balancer_policy = "Ribbon"
+    compiler_cfg.default_df_override = pybuda.DataFormat.Float16_b
+
+    class Repeat_interleave_model(torch.nn.Module):
+        def __init__(self, repeats, dims):
+            super().__init__()
+            self.repeats = repeats
+            self.dims = dims
+
+        def forward(self, input_tensor):
+            return torch.repeat_interleave(input_tensor, repeats = self.repeats, dim = self.dims)
+
+    model = Repeat_interleave_model(repeats=num_repeats, dims=repeat_dims)
+    model.eval()
+
+    # Create PyBuda module from PyTorch model
+    tt_model = pybuda.PyTorchModule(
+        "pt_repeat_interleave", model
+    )
+
+    input_sample = torch.rand(input_shape)
+
+    # Run inference on Tenstorrent device
+    verify_module(
+        tt_model,
+        input_shapes=[(input_sample.shape,)],
+        inputs=[(input_sample,)],
+        verify_cfg=VerifyConfig(
+            arch=test_device.arch,
+            devtype=test_device.devtype,
+            devmode=test_device.devmode,
+            test_kind=TestKind.INFERENCE,
+            verify_pybuda_codegen_vs_framework=True,
+            verify_tvm_compile=True,
+        ),
+    )
+