Add paddle.tensor.tensor.Tensor.squeeze function

ivy/functional/frontends/paddle/tensor/tensor.py

@@ -617,3 +617,19 @@ def std(self, axis=None, unbiased=True, keepdim=False, name=None):
         return paddle_frontend.Tensor(
             ivy.std(self._ivy_array, axis=axis, keepdims=keepdim)
         )
+
+    @with_supported_dtypes(
+        {
+            "2.5.1 and below": (
+                "float32",
+                "float64",
+                "bool",
+                "int8",
+                "int32",
+                "int64"
+            )
+        },
+        "paddle",
+    )
+    def squeeze(self, axis=None, name=None):
+        return paddle_frontend.Tensor(ivy.squeeze(self._ivy_array, axis=axis))

ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_tensor.py

@@ -2795,3 +2795,38 @@ def test_paddle_tensor_numel(
         frontend=frontend,
         on_device=on_device,
     )
+
+
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="paddle.to_tensor",
+    method_name="squeeze",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid"),
+        min_num_dims=1,
+    ),
+)
+def test_paddle_tensor_squeeze(
+    dtype_and_x,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    frontend,
+    backend_fw,
+    on_device,
+):
+    input_dtype, x = dtype_and_x
+    helpers.test_frontend_method(
+        init_input_dtypes=input_dtype,
+        init_all_as_kwargs_np={
+            "data": x[0],
+        },
+        method_input_dtypes=input_dtype,
+        backend_to_test=backend_fw,
+        method_all_as_kwargs_np={},
+        frontend_method_data=frontend_method_data,
+        init_flags=init_flags,
+        method_flags=method_flags,
+        frontend=frontend,
+        on_device=on_device,
+    )