diff --git a/forge/test/operators/pytorch/eltwise_unary/__init__.py b/forge/test/operators/pytorch/eltwise_unary/__init__.py
index 2332467ef..5f28381a7 100644
--- a/forge/test/operators/pytorch/eltwise_unary/__init__.py
+++ b/forge/test/operators/pytorch/eltwise_unary/__init__.py
@@ -1,3 +1,11 @@
 # SPDX-FileCopyrightText: © 2024 Tenstorrent AI ULC
 
 # SPDX-License-Identifier: Apache-2.0
+
+from .models import ModelFromAnotherOp, ModelDirect, ModelConstEvalPass
+
+__all__ = [
+    "ModelFromAnotherOp",
+    "ModelDirect",
+    "ModelConstEvalPass",
+]
diff --git a/forge/test/operators/pytorch/eltwise_unary/models.py b/forge/test/operators/pytorch/eltwise_unary/models.py
new file mode 100644
index 000000000..dad298fb8
--- /dev/null
+++ b/forge/test/operators/pytorch/eltwise_unary/models.py
@@ -0,0 +1,45 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent AI ULC
+
+# SPDX-License-Identifier: Apache-2.0
+
+import torch
+import torch.nn as nn
+
+from forge.op_repo import TensorShape
+
+
+class ModelFromAnotherOp(nn.Module):
+    def __init__(self, operator, kwargs):
+        super().__init__()
+        self.testname = "Element_wise_unary_operators_test_op_src_from_another_op"
+        self.operator = operator
+        self.kwargs = kwargs
+
+    def forward(self, x):
+        xx = torch.add(x, x)
+        return self.operator(xx, **self.kwargs)
+
+
+class ModelDirect(nn.Module):
+    def __init__(self, operator, kwargs):
+        super().__init__()
+        self.testname = "Element_wise_unary_operators_test_op_src_from_host"
+        self.operator = operator
+        self.kwargs = kwargs
+
+    def forward(self, x):
+        return self.operator(x, **self.kwargs)
+
+
+class ModelConstEvalPass(nn.Module):
+    def __init__(self, operator, shape: TensorShape, kwargs):
+        super().__init__()
+        self.testname = "Element_wise_unary_operators_test_op_src_const_eval_pass"
+        self.operator = operator
+        self.kwargs = kwargs
+        self.c = (torch.rand(shape, requires_grad=False) - 0.5).detach()
+
+    def forward(self, x):
+        cc = self.operator(self.c, **self.kwargs)
+        xx = self.operator(x, **self.kwargs)
+        return torch.add(xx, cc)
diff --git a/forge/test/operators/pytorch/eltwise_unary/test_unary.py b/forge/test/operators/pytorch/eltwise_unary/test_unary.py
index 568d7e50d..3b24d8489 100644
--- a/forge/test/operators/pytorch/eltwise_unary/test_unary.py
+++ b/forge/test/operators/pytorch/eltwise_unary/test_unary.py
@@ -49,11 +49,7 @@
 #    (/) Reuse inputs for selected operators
 
 
-import pytest
 import torch
-import torch.nn as nn
-import forge
-from forge.op_repo import TensorShape
 
 from typing import List, Dict
 from loguru import logger
@@ -69,42 +65,7 @@
 from test.operators.utils import TestCollectionCommon
 from test.operators.utils import ValueRanges
 
-
-class ModelFromAnotherOp(nn.Module):
-    def __init__(self, operator, kwargs):
-        super().__init__()
-        self.testname = "Element_wise_unary_operators_test_op_src_from_another_op"
-        self.operator = operator
-        self.kwargs = kwargs
-
-    def forward(self, x):
-        xx = torch.add(x, x)
-        return self.operator(xx, **self.kwargs)
-
-
-class ModelDirect(nn.Module):
-    def __init__(self, operator, kwargs):
-        super().__init__()
-        self.testname = "Element_wise_unary_operators_test_op_src_from_host"
-        self.operator = operator
-        self.kwargs = kwargs
-
-    def forward(self, x):
-        return self.operator(x, **self.kwargs)
-
-
-class ModelConstEvalPass(nn.Module):
-    def __init__(self, operator, shape: TensorShape, kwargs):
-        super().__init__()
-        self.testname = "Element_wise_unary_operators_test_op_src_const_eval_pass"
-        self.operator = operator
-        self.kwargs = kwargs
-        self.c = (torch.rand(shape, requires_grad=False) - 0.5).detach()
-
-    def forward(self, x):
-        cc = self.operator(self.c, **self.kwargs)
-        xx = self.operator(x, **self.kwargs)
-        return torch.add(xx, cc)
+from .models import ModelFromAnotherOp, ModelDirect, ModelConstEvalPass
 
 
 class TestVerification:
diff --git a/forge/test/operators/pytorch/tm/test_reshape.py b/forge/test/operators/pytorch/tm/test_reshape.py
new file mode 100644
index 000000000..9ccb5ea4e
--- /dev/null
+++ b/forge/test/operators/pytorch/tm/test_reshape.py
@@ -0,0 +1,358 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent AI ULC
+
+# SPDX-License-Identifier: Apache-2.0
+
+# TODO: Add test plan header here
+
+import forge
+import math
+import torch
+import pytest
+import random
+import os
+
+from typing import List, Dict
+from loguru import logger
+
+from forge.verify.config import VerifyConfig
+
+from forge.verify.value_checkers import AllCloseValueChecker
+from forge.verify.verify import verify as forge_verify
+
+from test.operators.utils import InputSourceFlags, VerifyUtils
+from test.operators.utils import InputSource
+from test.operators.utils import TestVector
+from test.operators.utils import TestPlan
+from test.operators.utils import TestPlanUtils
+from test.operators.utils import FailingReasons
+from test.operators.utils.compat import TestDevice
+from test.operators.utils import TestCollection
+from test.operators.utils import TestCollectionCommon
+from test.operators.utils import ValueRanges
+
+from test.operators.pytorch.eltwise_unary import ModelFromAnotherOp, ModelDirect, ModelConstEvalPass
+
+
+def prime_factors(n):
+    """Return the prime factors of a given number."""
+    factors = []
+    while n % 2 == 0:
+        factors.append(2)
+        n //= 2
+
+    for i in range(3, int(math.sqrt(n)) + 1, 2):
+        while n % i == 0:
+            factors.append(i)
+            n //= i
+
+    if n > 2:
+        factors.append(n)
+
+    return factors
+
+
+def generate_random_shape(source_shape, max_dims=4):
+    """
+    Generates a random shape with the same volume as the source shape.
+    Args:
+        source_shape (tuple): The original shape of the tensor.
+        max_dims (int, optional): The maximum number of dimensions for the generated shape. Defaults to 4.
+    Returns:
+        tuple: A randomly generated shape with the same volume as the source shape.
+    Raises:
+        ValueError: If the volume or number of dimensions is not a positive integer.
+        ValueError: If the generated shape does not match the desired volume.
+    Notes:
+        - The function ensures that the product of the dimensions of the generated shape equals the product of the dimensions of the source shape.
+        - If the generated shape is the same as the source shape, an additional dimension with size 1 is appended to the shape.
+    """
+
+    rng = random.Random(math.prod(source_shape) + 1)
+
+    vol = math.prod(source_shape)
+    num_dims = rng.randint(1, max_dims)
+
+    if vol <= 0 or num_dims <= 0:
+        raise ValueError("Volume and number of dimensions must be positive integers.")
+
+    # Step 1: Get the prime factors of vol
+    factors = prime_factors(vol)
+
+    # Step 2: Start with dimensions initialized to 1
+    shape = [1] * num_dims
+
+    # Step 3: Randomly assign factors to dimensions
+    for factor in factors:
+        random_dim = rng.randint(0, num_dims - 1)
+        shape[random_dim] *= factor
+
+    # Step 4: Ensure the final shape has the correct volume
+    if math.prod(shape) != vol:
+        raise ValueError("Generated shape does not match the desired volume.")
+
+    return tuple(shape)
+
+
+class TestVerification:
+
+    MODEL_TYPES = {
+        InputSource.FROM_ANOTHER_OP: ModelFromAnotherOp,
+        InputSource.FROM_HOST: ModelDirect,
+        InputSource.FROM_DRAM_QUEUE: ModelDirect,
+        InputSource.CONST_EVAL_PASS: ModelConstEvalPass,
+    }
+
+    @classmethod
+    def verify(
+        cls,
+        test_device: TestDevice,
+        test_vector: TestVector,
+        input_params: List[Dict] = [],
+        warm_reset: bool = False,
+    ):
+
+        input_source_flag: InputSourceFlags = None
+        if test_vector.input_source in (InputSource.FROM_DRAM_QUEUE,):
+            input_source_flag = InputSourceFlags.FROM_DRAM
+
+        operator = getattr(torch, test_vector.operator)
+        kwargs = test_vector.kwargs if test_vector.kwargs else {}
+
+        model_type = cls.MODEL_TYPES[test_vector.input_source]
+        pytorch_model = (
+            model_type(operator, test_vector.input_shape, kwargs)
+            if test_vector.input_source in (InputSource.CONST_EVAL_PASS,)
+            else model_type(operator, kwargs)
+        )
+
+        input_shapes = tuple([test_vector.input_shape])
+
+        logger.trace(f"***input_shapes: {input_shapes}")
+
+        VerifyUtils.verify(
+            model=pytorch_model,
+            test_device=test_device,
+            input_shapes=input_shapes,
+            input_params=input_params,
+            input_source_flag=input_source_flag,
+            dev_data_format=test_vector.dev_data_format,
+            math_fidelity=test_vector.math_fidelity,
+            warm_reset=warm_reset,
+            value_range=ValueRanges.SMALL,
+            deprecated_verification=False,
+            verify_config=VerifyConfig(value_checker=AllCloseValueChecker()),
+        )
+
+
+class TestParamsData:
+
+    __test__ = False
+
+    test_plan: TestPlan = None
+
+    # fmt: off
+    specific_reshapes = {
+        # Flatten Reshapes:
+        (8, 8, 8):        [(512,)],
+        (1, 2, 2, 2):     [(4, 2), (8,), ],
+        # Dynamic Shape Reshapes:
+        (1, 49, 2304):    [(1, -1, 3, 24, 32),
+                           (1, 49, 6, -1),
+                           (-1, ),
+                           (1, -1)],
+        (3, 4, 5):        [(3, -1), (-1, 15)],
+        (2, 2, 2, 2):     [(4, -1), (8, -1)],
+        # Collapse Rank Reshapes:
+        (2, 3, 4, 5):     [(6, 4, 5), (4, 6, 5)],
+        (2, 2, 3, 4):     [(4, 3, 4), (2, 6, 4)],
+        (3, 3, 3, 3):     [(9, 3, 3), (3, 9, 3)],
+        (1, 2, 3, 4, 5):  [(2, 3, 4, 5), (6, 4, 5)],
+        (2, 2, 2, 2, 2):  [(4, 2, 2, 2), (8, 2, 2)],
+        (2, 3, 4, 8):     [(6, 4, 8), (24, 8)],
+        # Expand Rank Reshapes:
+        (6, 4, 5):        [(2, 3, 4, 5),
+                           (1, 6, 4, 5),
+                           (2, 3, 2, 2, 5)],
+        (12, 8, 10):      [(2, 6, 8, 10),
+                           (2, 2, 3, 8, 10),
+                           (1, 3, 4, 8, 10)],
+        # Channel Manipulation Reshapes:
+        (1, 32, 2560):    [(1, 32, 20, 128),
+                           (1, 32, 40, 64),
+                           (1, 32, 10, 256),
+                           (1, 32, 5, 512),
+                           (1, 32, 80, 32)],
+        (1, 64, 1280):    [(1, 64, 10, 128),
+                           (1, 64, 20, 64),
+                           (1, 64, 5, 256),
+                           (1, 64, 40, 32),
+                           (1, 64, 80, 16)],
+        # Edge Case Reshapes:
+        (0,):             [(0, 0, 1),
+                           (0, 1, 0),
+                           (1, 0, 0),
+                           (0, 0, 10)],
+    }
+    # fmt: on
+
+    @classmethod
+    def generate_random_kwargs(cls, test_vector: TestVector):
+        """
+        This method generates a random target shape based on the input shape of the
+        test vector and a seed value derived from the sum of the elements in the
+        input shape. The generated target shape is then returned as a dictionary
+        within a list.
+        Args:
+            test_vector (TestVector): The test vector containing the input shape.
+        Returns:
+            list: A list containing a dictionary with the generated target shape.
+        """
+
+        input_shape = test_vector.input_shape
+        seed = 0
+        for i in range(len(input_shape)):
+            seed += input_shape[i]
+        target_shape = generate_random_shape(test_vector.input_shape)
+        return [
+            {"shape": target_shape},
+        ]
+
+    @classmethod
+    def generate_specific_kwargs(cls, test_vector: TestVector):
+        """
+        Generate specific keyword arguments for reshaping tensors based on the given test vector.
+        Args:
+            cls: The class that contains specific reshapes mapping.
+            test_vector (TestVector): An instance of TestVector containing the input shape.
+        Returns:
+            list: A list of dictionaries, each containing a target shape for reshaping.
+        """
+
+        input_shape = test_vector.input_shape
+        target_shapes = []
+        for item in cls.specific_reshapes[input_shape]:
+            target_shapes.append({"shape": item})
+        return target_shapes
+
+
+TestParamsData.test_plan = TestPlan(
+    verify=lambda test_device, test_vector: TestVerification.verify(
+        test_device,
+        test_vector,
+    ),
+    collections=[
+        # Test operators with all shapes and input sources collection:
+        TestCollection(
+            operators=["reshape"],
+            input_sources=TestCollectionCommon.all.input_sources,
+            input_shapes=TestCollectionCommon.all.input_shapes,
+            kwargs=lambda test_vector: TestParamsData.generate_random_kwargs(test_vector),
+        ),
+        # Test Data formats collection:
+        TestCollection(
+            operators=["reshape"],
+            input_sources=TestCollectionCommon.single.input_sources,
+            input_shapes=TestCollectionCommon.single.input_shapes,
+            kwargs=lambda test_vector: TestParamsData.generate_random_kwargs(test_vector),
+            dev_data_formats=[
+                item
+                for item in TestCollectionCommon.all.dev_data_formats
+                if item not in TestCollectionCommon.single.dev_data_formats
+            ],
+            math_fidelities=TestCollectionCommon.single.math_fidelities,
+        ),
+        # Test Math fidelities collection:
+        TestCollection(
+            operators=["reshape"],
+            input_sources=TestCollectionCommon.single.input_sources,
+            input_shapes=TestCollectionCommon.single.input_shapes,
+            kwargs=lambda test_vector: TestParamsData.generate_random_kwargs(test_vector),
+            dev_data_formats=TestCollectionCommon.single.dev_data_formats,
+            math_fidelities=TestCollectionCommon.all.math_fidelities,
+        ),
+        # Test specific classes of reshape operations collection:
+        TestCollection(
+            operators=["reshape"],
+            input_sources=TestCollectionCommon.all.input_sources,
+            input_shapes=TestParamsData.specific_reshapes.keys(),
+            kwargs=lambda test_vector: TestParamsData.generate_specific_kwargs(test_vector),
+        ),
+    ],
+    failing_rules=[
+        TestCollection(
+            criteria=lambda test_vector: test_vector.get_id()
+            in TestPlanUtils.load_test_ids_from_file(
+                f"{os.path.dirname(__file__)}/test_reshape_ids_failed_allclose_value_checker.txt"
+            ),
+            failing_reason=FailingReasons.DATA_MISMATCH,
+        ),
+        TestCollection(
+            input_shapes=[(1, 10000)],
+            failing_reason=FailingReasons.INFERENCE_FAILED,
+        ),
+        TestCollection(
+            input_shapes=[
+                (100, 100),
+                (1000, 100),
+                (89, 3),
+                (1, 64, 1),
+                (1, 100, 100),
+                (11, 17, 41),
+                (1, 2, 3, 4),
+                (1, 11, 45, 17),
+                (1, 11, 17, 41),
+                (1, 13, 89, 3),
+                (8, 1, 10, 1000),
+                (11, 32, 32, 64),
+                (8, 8, 8),
+            ],
+            failing_reason=FailingReasons.INFERENCE_FAILED,
+        ),
+        TestCollection(
+            input_shapes=[(1, 2, 2, 2)],
+            criteria=lambda test_vector: test_vector.kwargs is not None
+            and "shape" in test_vector.kwargs
+            and test_vector.kwargs["shape"] == (8,),
+            failing_reason=FailingReasons.INFERENCE_FAILED,
+        ),
+        TestCollection(
+            input_shapes=[(1, 49, 2304)],
+            criteria=lambda test_vector: test_vector.kwargs is not None
+            and "shape" in test_vector.kwargs
+            and test_vector.kwargs["shape"] == (-1,),
+            failing_reason=FailingReasons.INFERENCE_FAILED,
+        ),
+        TestCollection(
+            input_shapes=[(0,)],
+            failing_reason=FailingReasons.UNSUPPORTED_DIMENSION,
+        ),
+    ],
+)
+
+
+def get_test_plans() -> List[TestPlan]:
+    return [TestParamsData.test_plan]
+
+
+@pytest.mark.parametrize(
+    "input_shape, target_shape",
+    [
+        ((1, 4), (4, 1, 1)),
+        ((1, 100), (10, 10)),
+        ((1, 3, 4), (6, 2)),
+    ],
+)
+def test_reshape_accuracy(input_shape, target_shape):
+
+    inputs = [torch.ones(input_shape) - 0.1]  # value: 0.9000
+
+    pytorch_model = ModelDirect(torch.reshape, {"shape": target_shape})
+    compiled_model = forge.compile(pytorch_model, sample_inputs=inputs)
+
+    # forge.verify.verify.verify as forge_verify:
+    forge_verify(
+        inputs,
+        pytorch_model,
+        compiled_model,
+        verify_cfg=VerifyConfig(value_checker=AllCloseValueChecker()),
+    )
diff --git a/forge/test/operators/pytorch/tm/test_reshape_ids_failed_allclose_value_checker.txt b/forge/test/operators/pytorch/tm/test_reshape_ids_failed_allclose_value_checker.txt
new file mode 100644
index 000000000..aaac37678
--- /dev/null
+++ b/forge/test/operators/pytorch/tm/test_reshape_ids_failed_allclose_value_checker.txt
@@ -0,0 +1,316 @@
+reshape-FROM_ANOTHER_OP-{'shape': (4, 1, 1)}-(1, 4)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (10, 10)}-(1, 100)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (500, 1, 1)}-(1, 500)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (16, 2, 1, 2)}-(1, 64)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (2, 48)}-(1, 96)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (3, 1, 1, 1)}-(1, 3)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (4, 3, 1)}-(3, 4)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (51, 15)}-(45, 17)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (16, 2, 1, 2)}-(64, 1)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (10000, 1)}-(10, 1000)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (2480, 4)}-(9920, 1)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (625, 8, 2)}-(10000, 1)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (2, 16, 64)}-(32, 64)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (1, 64, 1, 240)}-(160, 96)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (1, 697)}-(17, 41)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (6, 2)}-(1, 3, 4)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (15, 17, 3)}-(1, 45, 17)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (23, 1, 1, 1)}-(1, 1, 23)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (50, 16, 125)}-(1, 1000, 100)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (2500, 1, 4)}-(1, 10, 1000)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (2, 4960)}-(1, 9920, 1)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (5, 125, 4, 4)}-(1, 10000, 1)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (4, 512)}-(1, 32, 64)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (1, 1536, 10)}-(1, 160, 96)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (1, 697)}-(1, 17, 41)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (1, 3, 1, 89)}-(1, 89, 3)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (3, 1, 8)}-(2, 3, 4)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (255, 11, 3)}-(11, 45, 17)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (1, 1, 253)}-(11, 1, 23)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (22, 4, 8)}-(11, 64, 1)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (2, 1250, 400)}-(100, 100, 100)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (1, 10, 40, 2500)}-(10, 1000, 100)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (25, 4000)}-(10, 10000, 1)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (1, 65536)}-(32, 32, 64)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (8, 3840, 2, 16)}-(64, 160, 96)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (89, 1, 39)}-(13, 89, 3)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (1, 1, 253)}-(1, 11, 1, 23)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (11, 1, 64)}-(1, 11, 64, 1)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (25000, 20, 2, 1)}-(1, 100, 100, 100)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (1250, 800)}-(1, 10, 1000, 100)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (25, 200, 2)}-(1, 1, 10, 1000)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (1, 320, 1, 31)}-(1, 1, 9920, 1)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (10, 250, 10, 4)}-(1, 10, 10000, 1)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (4, 2, 8192)}-(1, 32, 32, 64)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (15360, 16, 4)}-(1, 64, 160, 96)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (51, 1, 495)}-(3, 11, 45, 17)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (6, 1, 8)}-(2, 2, 3, 4)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (1012, 1)}-(4, 11, 1, 23)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (110, 2, 16, 1)}-(5, 11, 64, 1)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (32, 93750, 2)}-(6, 100, 100, 100)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (625, 448, 1, 25)}-(7, 10, 1000, 100)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (64, 5, 1, 279)}-(9, 1, 9920, 1)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (50, 625, 32, 1)}-(10, 10, 10000, 1)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (40, 294912)}-(12, 64, 160, 96)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (143, 697)}-(13, 11, 17, 41)-None-None
+reshape-FROM_ANOTHER_OP-{'shape': (7, 2, 3471)}-(14, 13, 89, 3)-None-None
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+reshape-CONST_EVAL_PASS-{'shape': (3, -1)}-(3, 4, 5)-None-None
+reshape-CONST_EVAL_PASS-{'shape': (-1, 15)}-(3, 4, 5)-None-None
+reshape-CONST_EVAL_PASS-{'shape': (4, -1)}-(2, 2, 2, 2)-None-None
+reshape-CONST_EVAL_PASS-{'shape': (8, -1)}-(2, 2, 2, 2)-None-None
+reshape-CONST_EVAL_PASS-{'shape': (4, 6, 5)}-(2, 3, 4, 5)-None-None
+reshape-CONST_EVAL_PASS-{'shape': (2, 6, 4)}-(2, 2, 3, 4)-None-None
+reshape-CONST_EVAL_PASS-{'shape': (3, 9, 3)}-(3, 3, 3, 3)-None-None
+reshape-CONST_EVAL_PASS-{'shape': (24, 8)}-(2, 3, 4, 8)-None-None
+reshape-CONST_EVAL_PASS-{'shape': (2, 3, 2, 2, 5)}-(6, 4, 5)-None-None
+reshape-CONST_EVAL_PASS-{'shape': (1, 32, 20, 128)}-(1, 32, 2560)-None-None
+reshape-CONST_EVAL_PASS-{'shape': (1, 32, 40, 64)}-(1, 32, 2560)-None-None
+reshape-CONST_EVAL_PASS-{'shape': (1, 32, 10, 256)}-(1, 32, 2560)-None-None
+reshape-CONST_EVAL_PASS-{'shape': (1, 32, 5, 512)}-(1, 32, 2560)-None-None
+reshape-CONST_EVAL_PASS-{'shape': (1, 32, 80, 32)}-(1, 32, 2560)-None-None
+reshape-CONST_EVAL_PASS-{'shape': (1, 64, 10, 128)}-(1, 64, 1280)-None-None
+reshape-CONST_EVAL_PASS-{'shape': (1, 64, 20, 64)}-(1, 64, 1280)-None-None
+reshape-CONST_EVAL_PASS-{'shape': (1, 64, 5, 256)}-(1, 64, 1280)-None-None
+reshape-CONST_EVAL_PASS-{'shape': (1, 64, 40, 32)}-(1, 64, 1280)-None-None
+reshape-CONST_EVAL_PASS-{'shape': (1, 64, 80, 16)}-(1, 64, 1280)-None-None
diff --git a/forge/test/operators/utils/compat.py b/forge/test/operators/utils/compat.py
index 9a546ce5e..db64b3c1a 100644
--- a/forge/test/operators/utils/compat.py
+++ b/forge/test/operators/utils/compat.py
@@ -13,6 +13,8 @@
 from forge import ForgeModule, Module, DepricatedVerifyConfig
 from forge.op_repo import TensorShape
 from forge.verify.compare import compare_with_golden
+from forge.verify.verify import verify
+from forge.verify.config import VerifyConfig
 
 from .datatypes import OperatorParameterTypes, ValueRanges, ValueRange
 
@@ -271,7 +273,7 @@ def create_torch_inputs(
     return inputs
 
 
-def verify_module_for_inputs(
+def verify_module_for_inputs_deprecated(
     model: Module,
     inputs: List[torch.Tensor],
     pcc: Optional[float] = None,
@@ -300,3 +302,15 @@ def verify_module_for_inputs(
         assert all(
             [compare_with_golden(golden=fo, calculated=co) for fo, co in zip(fw_out, co_out)]
         ), "PCC check failed"
+
+
+def verify_module_for_inputs(
+    model: Module,
+    inputs: List[torch.Tensor],
+    verify_config: Optional[VerifyConfig] = VerifyConfig(),
+    dev_data_format: forge.DataFormat = None,
+):
+
+    forge_inputs = [forge.Tensor.create_from_torch(input, dev_data_format=dev_data_format) for input in inputs]
+    compiled_model = forge.compile(model, sample_inputs=forge_inputs)
+    verify(inputs, model, compiled_model, verify_config)
diff --git a/forge/test/operators/utils/failing_reasons.py b/forge/test/operators/utils/failing_reasons.py
index 54ca3d4c9..4c456233e 100644
--- a/forge/test/operators/utils/failing_reasons.py
+++ b/forge/test/operators/utils/failing_reasons.py
@@ -102,6 +102,7 @@ def validate_exception_message(
         FailingReasons.DATA_MISMATCH: [
             lambda ex: isinstance(ex, AssertionError) and f"{ex}" == "PCC check failed",
             lambda ex: isinstance(ex, AssertionError) and f"{ex}".startswith("Data mismatch"),
+            lambda ex: isinstance(ex, ValueError) and f"{ex}".startswith("Data mismatch"),
         ],
         FailingReasons.UNSUPPORTED_SPECIAL_CASE: [
             lambda ex: isinstance(ex, AssertionError) and f"{ex}" == "PCC check failed",
@@ -158,6 +159,11 @@ def validate_exception_message(
             lambda ex: isinstance(ex, RuntimeError)
             and "tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/data_movement/transpose/device/transpose_op.cpp:106: input_tensor.get_dtype() == DataType::BFLOAT16 || input_tensor.get_dtype() == DataType::FLOAT32"
             in f"{ex}",
+            lambda ex: isinstance(ex, RuntimeError)
+            and "Statically allocated circular buffers on core range [(x=0,y=0) - (x=0,y=0)] grow to 2663200 B which is beyond max L1 size of 1499136 B"
+            in f"{ex}",
+            lambda ex: isinstance(ex, RuntimeError)
+            and "Index is out of bounds for the rank, should be between 0 and 0 however is 1" in f"{ex}",
         ],
     }
 
diff --git a/forge/test/operators/utils/utils.py b/forge/test/operators/utils/utils.py
index 9170ffd8f..91674879a 100644
--- a/forge/test/operators/utils/utils.py
+++ b/forge/test/operators/utils/utils.py
@@ -23,8 +23,10 @@
 from forge.config import _get_global_compiler_config
 from forge._C import MathFidelity
 
+from forge.verify.config import VerifyConfig
+
 from .compat import TestDevice
-from .compat import create_torch_inputs, verify_module_for_inputs
+from .compat import create_torch_inputs, verify_module_for_inputs, verify_module_for_inputs_deprecated
 from .datatypes import ValueRanges
 
 
@@ -126,6 +128,8 @@ def verify(
         value_range: Optional[ValueRanges] = None,
         random_seed: Optional[int] = None,
         warm_reset: bool = False,
+        deprecated_verification: bool = True,
+        verify_config: Optional[VerifyConfig] = VerifyConfig(),
     ):
         """Perform Forge verification on the model
 
@@ -141,6 +145,7 @@ def verify(
             value_range: Value range of input tensors
             random_seed: Random seed
             warm_reset: Warm reset the device before verification
+            deprecated_verification: Use deprecated verification method
         """
 
         cls.setup(
@@ -156,12 +161,20 @@ def verify(
             random_seed=random_seed,
         )
 
-        cls.verify_module_for_inputs(
-            model=model,
-            inputs=inputs,
-            pcc=pcc,
-            dev_data_format=dev_data_format,
-        )
+        if deprecated_verification:
+            cls.verify_module_for_inputs_deprecated(
+                model=model,
+                inputs=inputs,
+                pcc=pcc,
+                dev_data_format=dev_data_format,
+            )
+        else:
+            cls.verify_module_for_inputs(
+                model=model,
+                inputs=inputs,
+                verify_config=verify_config,
+                dev_data_format=dev_data_format,
+            )
 
     @classmethod
     def setup(
@@ -201,7 +214,7 @@ def create_torch_inputs(
         return inputs
 
     @classmethod
-    def verify_module_for_inputs(
+    def verify_module_for_inputs_deprecated(
         cls,
         model: Module,
         inputs: List[torch.Tensor],
@@ -209,13 +222,29 @@ def verify_module_for_inputs(
         dev_data_format: forge.DataFormat = None,
     ):
 
-        verify_module_for_inputs(
+        verify_module_for_inputs_deprecated(
             model=model,
             inputs=inputs,
             pcc=pcc,
             dev_data_format=dev_data_format,
         )
 
+    @classmethod
+    def verify_module_for_inputs(
+        cls,
+        model: Module,
+        inputs: List[torch.Tensor],
+        verify_config: Optional[VerifyConfig] = VerifyConfig(),
+        dev_data_format: forge.DataFormat = None,
+    ):
+
+        verify_module_for_inputs(
+            model=model,
+            inputs=inputs,
+            verify_config=verify_config,
+            dev_data_format=dev_data_format,
+        )
+
 
 class LoggerUtils:
     """Utility functions for logging"""