Add sample model with block_quantize nodes from qtorch

- This example script fails on torch_mlir.compile() API
- Compiles fine with no block_quantize in SimpleModel.forward()
- Having block_quantize only on the inputs tensors compiles fine

examples/block_quantize_experiment.py

@@ -0,0 +1,32 @@
+import torch
+import torch.nn as nn
+
+import qtorch
+from qtorch.quant import block_quantize
+
+import torch_mlir
+
+class SimpleModel(nn.Module):
+    def __init__(self, input_dim, output_size):
+        super(SimpleModel, self).__init__()
+        self.matmul = nn.Linear(input_dim, output_size)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        matmul_out = self.matmul(x.flatten(1))
+        quantized_matmul_out = block_quantize(matmul_out, wl=8, dim=0, rounding="nearest")
+        relu_out = self.relu(quantized_matmul_out)
+        return relu_out
+
+batches = 5
+input_dim = 64
+output_size = 4
+inputs = torch.randn(batches, input_dim)
+model = SimpleModel(input_dim, output_size)
+print("forward propagate results on inputs is:\n", model.forward(inputs))
+
+# quantized_inputs = block_quantize(inputs, wl=8, dim=0, rounding="nearest")
+# print("forward propagate of quantized inputs result is ", model.forward(quantized_inputs))
+
+module = torch_mlir.compile(model, inputs, output_type=torch_mlir.OutputType.TOSA)
+print("Module compiled to TOSA is:\n", module)