Added torch frontend function quantile() and its test 2.0 (#10573)

Co-authored-by: @AnnaTz

ivy/functional/frontends/torch/reduction_ops.py

@@ -162,3 +162,14 @@ def aminmax(input, *, dim=None, keepdim=False, out=None):
     "jax": ("float16", "bfloat16"),
     "tensorflow": ("float16", "bfloat16"),
 }
+
+@to_ivy_arrays_and_back
+def quantile(input, q, dim=None, keepdim=False, *, interpolation='linear', out=None):
+    return ivy.quantile(input, q, axis=dim, keepdims=keepdim, interpolation=interpolation, out=out)
+
+quantile.unsupported_dtypes = {
+    "torch": ("float16", "bfloat16"),
+    "numpy": ("float16", "bfloat16"),
+    "jax": ("float16", "bfloat16"),
+    "tensorflow": ("float16", "bfloat16"),
+}

ivy_tests/test_ivy/test_frontends/test_torch/test_reduction_ops.py

@@ -8,7 +8,7 @@
 from ivy_tests.test_ivy.test_functional.test_core.test_statistical import (
     statistical_dtype_values,
 )
-
+from ivy_tests.test_ivy.test_functional.test_experimental.test_core.test_statistical import statistical_dtype_values as statistical_dtype_values_experimental
 
 @handle_frontend_test(
     fn_tree="torch.dist",
@@ -666,3 +666,34 @@ def test_torch_aminmax(
         dim=axis,
         keepdim=keepdims,
     )
+
+@handle_frontend_test(
+    fn_tree="torch.quantile",
+    dtype_and_x=statistical_dtype_values_experimental(function="quantile"),
+    keepdims=st.booleans()
+)
+
+def test_torch_quantile(
+    *,
+    dtype_and_x,
+    keepdims,
+    on_device,
+    fn_tree,
+    frontend,
+    test_flags,
+):
+    input_dtype, x, axis, interpolation, q = dtype_and_x
+    if type(axis) is tuple:
+        axis=axis[0]
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        input=x[0],
+        q=q,
+        dim=axis,
+        keepdim=keepdims,
+        interpolation=interpolation[0]
+    )