Add UT for lstm model with large batch with clipping.

test/models/onnx/lstm_fwd_large_batch_with_clip.prototxt

+ir_version: 4
+producer_name: "nGraph ONNX Importer"
+graph {
+  node {
+    input: "X"
+    input: "W"
+    input: "R"
+    output: ""
+    output: "Y_h"
+    op_type: "LSTM"
+    attribute {
+      name: "clip"
+      f: 4.0
+      type: FLOAT
+    }
+    attribute {
+      name: "direction"
+      s: "forward"
+      type: STRING
+    }
+    attribute {
+      name: "hidden_size"
+      i: 3
+      type: INT
+    }
+    attribute {
+      name: "input_forget"
+      i: 0
+      type: INT
+    }
+  }
+  name: "compute_graph"
+  input {
+    name: "X"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 1
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "W"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 12
+          }
+          dim {
+            dim_value: 1
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "R"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 12
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "Y_h"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 32
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 7
+}

test/onnx/onnx_import_rnn.in.cpp

@@ -20,6 +20,7 @@
 #include <fstream>
 #include <iterator>
 #include <limits>
+#include <numeric>
 #include <sstream>
 #include <stdexcept>
 #include <vector>
@@ -203,3 +204,66 @@ NGRAPH_TEST(onnx_${BACKEND_NAME}, model_lstm_fwd_hardsigmoid_activation)
     test_case.set_tolerance(6);
     test_case.run();
 }
+
+NGRAPH_TEST(onnx_${BACKEND_NAME}, model_lstm_fwd_large_batch_with_clip)
+{
+    auto function = onnx_import::import_onnx_model(
+        file_util::path_join(SERIALIZED_ZOO, "onnx/lstm_fwd_large_batch_with_clip.prototxt"));
+
+    auto test_case = ngraph::test::NgraphTestCase(function, "${BACKEND_NAME}");
+
+    std::size_t seq_length = 2;
+    std::size_t batch_size = 32;
+    std::size_t input_size = 1;
+    std::size_t hidden_size = 3;
+
+    std::vector<float> in_X(seq_length * batch_size * input_size);
+    std::iota(std::begin(in_X), std::end(in_X), 1.f);
+    std::vector<float> in_R(4 * hidden_size * hidden_size, 0.1f);
+
+    // X
+    test_case.add_input<float>(in_X);
+    // W
+    test_case.add_input<float>(
+            {0.1f, 0.2f, 0.3f, 0.4f, 1.f, 2.f, 3.f, 4.f, 10.f, 11.f, 12.f, 13.f});
+    // R
+    test_case.add_input<float>(in_R);
+
+    // Y_h_data
+    test_case.add_expected_output<float>(
+        Shape{1, batch_size, hidden_size},
+        {0.88572926f, 0.89251395f, 0.89655037f,
+         0.89074291f, 0.90035688f, 0.90727429f,
+         0.89535827f, 0.90727429f, 0.91596163f,
+         0.89963124f, 0.91328279f, 0.9228067f,
+         0.90358195f, 0.91843507f, 0.92809163f,
+         0.90723279f, 0.9228067f, 0.93211437f,
+         0.91038955f, 0.92648469f, 0.93514718f,
+         0.91328279f, 0.92955856f, 0.93741938f,
+         0.91596163f, 0.93211437f, 0.9391149f,
+         0.91843507f, 0.93423112f, 0.94037686f,
+         0.92071318f, 0.9359791f, 0.94131462f,
+         0.9228067f, 0.93741938f, 0.94201073f,
+         0.92472679f, 0.9386042f, 0.94252713f,
+         0.92648469f, 0.9395777f, 0.94266769f,
+         0.92809163f, 0.94037686f, 0.94266769f,
+         0.92955856f, 0.94103248f, 0.94266769f,
+         0.93089609f, 0.94157007f, 0.94266769f,
+         0.93211437f, 0.94201073f, 0.94266769f,
+         0.93322302f, 0.94237184f, 0.94266769f,
+         0.93423112f, 0.94266769f, 0.94266769f,
+         0.93514718f, 0.94266769f, 0.94266769f,
+         0.9359791f, 0.94266769f, 0.94266769f,
+         0.93673424f, 0.94266769f, 0.94266769f,
+         0.93741938f, 0.94266769f, 0.94266769f,
+         0.93804079f, 0.94266769f, 0.94266769f,
+         0.9386042f, 0.94266769f, 0.94266769f,
+         0.9391149f, 0.94266769f, 0.94266769f,
+         0.9395777f, 0.94266769f, 0.94266769f,
+         0.93999702f, 0.94266769f, 0.94266769f,
+         0.94037686f, 0.94266769f, 0.94266769f,
+         0.94072091f, 0.94266769f, 0.94266769f,
+         0.94103248f, 0.94266769f, 0.94266769f});
+
+    test_case.run();
+}