🐛 [Bug] Implement dynamic batch and dynamic shapes support for layer norm converter

Bug Description

Implement dynamic batch and dynamic shapes support for layer norm converter. Add the following testcase once it is implemented

def test_layernorm_with_dynamic_shape(self):
        class LayerNorm(torch.nn.Module):
            def forward(self, x):
                return torch.ops.aten.layer_norm.default(
                    x,
                    torch.tensor([3, 224, 224]),
                    torch.ones((3, 224, 224)),
                    torch.zeros((3, 224, 224)),
                    1e-05,
                    True,
                )

        input_specs = [
            Input(
                shape=(-1, 3, 224, 224),
                dtype=torch.float32,
                shape_ranges=[((1, 3, 224, 224), (1, 3, 224, 224), (2, 3, 224, 224))],
            ),
        ]

        self.run_test_with_dynamic_shape(
            LayerNorm(),
            input_specs,
        )

To Reproduce

Steps to reproduce the behavior:

Expected behavior

Environment

Build information about Torch-TensorRT can be found by turning on debug messages

• Torch-TensorRT Version (e.g. 1.0.0):
• PyTorch Version (e.g. 1.0):
• CPU Architecture:
• OS (e.g., Linux):
• How you installed PyTorch (conda, pip, libtorch, source):
• Build command you used (if compiling from source):
• Are you using local sources or building from archives:
• Python version:
• CUDA version:
• GPU models and configuration:
• Any other relevant information:

Additional context

Start by locating the layer norm converter and existing dynamic-shape converter tests. Add the supplied test_layernorm_with_dynamic_shape case, then run the relevant converter test suite and confirm dynamic batch and shape inputs compile and execute successfully.