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platforms/core-configuration/configuration-cache/src/main/kotlin/org/gradle/internal/cc/impl/problems/JsonModelWriter.kt
var first = true list.forEach { if (first) first = false else comma() body(it) } endArray() } private fun beginArray() { write('[') } private fun endArray() { write(']') } private fun property(name: String, value: String) { property(name) { jsonString(value) }
Registered: Wed Jun 12 18:38:38 UTC 2024 - Last Modified: Sat Jun 08 11:29:25 UTC 2024 - 8.8K bytes - Viewed (0) -
platforms/software/dependency-management/src/main/java/org/gradle/api/internal/artifacts/ivyservice/ivyresolve/parser/GradleModuleMetadataParser.java
reader.beginArray(); while (reader.peek() != JsonToken.END_ARRAY) { consumeVariant(reader, metadata); } reader.endArray(); } private void consumeVariant(JsonReader reader, MutableModuleComponentResolveMetadata metadata) throws IOException { String variantName = null;
Registered: Wed Jun 12 18:38:38 UTC 2024 - Last Modified: Tue Mar 05 02:07:04 UTC 2024 - 31.2K bytes - Viewed (0) -
platforms/extensibility/plugin-development/src/main/java/org/gradle/plugin/devel/tasks/internal/ValidationProblemSerialization.java
out.name("subcategories").beginArray(); for (String sc : value.getSubcategories()) { out.value(sc); } out.endArray(); out.endObject(); } @Override public ProblemCategory read(JsonReader in) throws IOException { in.beginObject(); String namespace = null;
Registered: Wed Jun 12 18:38:38 UTC 2024 - Last Modified: Tue May 28 09:03:53 UTC 2024 - 27.7K bytes - Viewed (0) -
testing/internal-integ-testing/src/main/groovy/org/gradle/test/fixtures/GradleModuleMetadata.groovy
private List<Object> readArray(JsonReader reader) { List<Object> values = [] reader.beginArray() while (reader.peek() != JsonToken.END_ARRAY) { Object value = readAny(reader) values.add(value) } reader.endArray() return values } static Map<String, String> normalizeForTests(Map<String, ?> attributes) {
Registered: Wed Jun 12 18:38:38 UTC 2024 - Last Modified: Thu Apr 04 07:21:38 UTC 2024 - 20K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/representative_dataset.py
from tensorflow.python.types import core from tensorflow.python.util import tf_export # A representative sample is a map of: input_key -> input_value. # Ex.: {'dense_input': tf.constant([1, 2, 3])} # Ex.: {'x1': np.ndarray([4, 5, 6]} RepresentativeSample = Mapping[str, core.TensorLike] # A representative dataset is an iterable of representative samples. RepresentativeDataset = Iterable[RepresentativeSample]
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 22 22:55:22 UTC 2024 - 14.2K bytes - Viewed (0) -
src/main/java/org/codelibs/fess/sso/oic/OpenIdConnectAuthenticator.java
Registered: Wed Jun 12 13:08:18 UTC 2024 - Last Modified: Thu Feb 22 01:37:57 UTC 2024 - 11.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/representative_dataset_test.py
sample, signature_def ) input_tensor_data = input_tensor.eval() self.assertLen(feed_dict, 1) self.assertIn('input:0', feed_dict) self.assertIsInstance(feed_dict['input:0'], np.ndarray) self.assertAllEqual(feed_dict['input:0'], input_tensor_data) @test_util.deprecated_graph_mode_only def test_create_feed_dict_from_input_data_empty(self): signature_def = meta_graph_pb2.SignatureDef(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jan 04 07:35:19 UTC 2024 - 11.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/calibrator/calibration_algorithm.py
self._hist_mids = np.linspace(first_mid, last_mid, self._num_bins) def _get_dequantized_hist_mids_after_quantize( self, quant_min: float, quant_max: float ) -> np.ndarray: """Quantizes and dequantizes hist_mids using quant_min and quant_max. Quantization converts the range of numbers from [quant_min, quant_max] to [0, 2^num_bits - 1]. Values less than quant_min are converted to 0, and
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Mar 11 19:29:56 UTC 2024 - 14.7K bytes - Viewed (0) -
RELEASE.md
[`tf.experimental.numpy`](https://www.tensorflow.org/api_docs/python/tf/experimental/numpy), which is a NumPy-compatible API for writing TF programs. This module provides class `ndarray`, which mimics the `ndarray` class in NumPy, and wraps an immutable `tf.Tensor` under the hood. A subset of NumPy functions (e.g. `numpy.add`) are provided. Their inter-operation with TF facilities is seamless in most cases. See
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 23:24:08 UTC 2024 - 730.3K bytes - Viewed (0)