feat(torch): torch.histc function added to torch front-end

ivy/functional/frontends/torch/miscellaneous_ops.py

@@ -541,3 +541,16 @@ def view_as_real(input):
     re_part = ivy.real(input)
     im_part = ivy.imag(input)
     return ivy.stack((re_part, im_part), axis=-1)
+
+
+@to_ivy_arrays_and_back
+def histc(input, bins=100, min=0, max=0, *, out= None):
+    input_ivy = ivy.flatten(input) # torch.histc results in a 1D tensor so I flattened it out.
+    if min ==0 and max ==0:
+        min = ivy.min(input_ivy); max = ivy.max(input_ivy)
+    if min == max:
+        range = (min - 1e-2 , min + 1e-2)
+    else:
+        range = (min , max)
+
+    return ivy.histogram(input_ivy, bins = bins, range=range, axis =0)

ivy_tests/test_ivy/test_frontends/test_torch/test_miscellaneous_ops.py

@@ -1824,3 +1824,31 @@ def test_torch_view_as_real(
         on_device=on_device,
         input=np.asarray(x[0], dtype=input_dtype[0]),
     )
+
+
+# histc
+@handle_frontend_test(
+        fn_tree= "torch.histc",
+        dtype_and_x = helpers.dtype_and_values(
+            available_dtypes= helpers.get_dtypes("float"),
+        ),
+    )
+def test_torch_histc(
+    *,
+    dtype_and_x,
+    on_device,
+    fn_tree,
+    frontend,
+    backend_fw,
+    test_flags,
+):
+    input_dtype , x = dtype_and_x
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        frontend=frontend,
+        backend_to_test=backend_fw,
+        test_flags = test_flags,
+        fn_tree= fn_tree,
+        on_device=on_device,
+        input = x[0],
+    )