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docs/en/data/sponsors_badge.yml
- nihpo - armand-sauzay - databento-bot - databento - nanram22 - Flint-company - porter-dev - fern-api - ndimares - svixhq - Alek99 - codacy - zanfaruqui - scalar - bump-sh - andrew-propelauth
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tensorflow/c/eager/unified_api_test.cc
} public: bool UseMlir() const { return strcmp(std::get<0>(GetParam()), "mlir") == 0; } bool UseFunction() const { return std::get<2>(GetParam()); } }; // Checks that inputs[0] is a scalar. Status TestScalarShape(AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> inputs, absl::Span<AbstractTensorHandle*> outputs) { PartialTensorShape shape;
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tensorflow/c/c_api_function_test.cc
* v */ // Define TF_Operation* c = ScalarConst(10, func_graph_, s_, "scalar10"); Define(-1, {}, {}, {c}, {}); // Use, run, and verify TF_Operation* func_op = Use({}); Run({}, func_op, 10); VerifyFDef({"scalar10_0"}, {}, {{"scalar10", DT_INT32}}, {{"scalar10_0:output:0", "scalar10"}}, {}); } TEST_F(CApiFunctionTest, OneOp_OneInput_OneOutput) { /*
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tensorflow/c/eager/unified_api_testutil.h
TestTensorHandleWithDims<T, datatype>(eager_ctx, data, dims, num_dims); *tensor = unwrap(TF_CreateAbstractTensorFromEagerTensor(input_eager, status.get())); return absl::OkStatus(); } // Return a scalar tensor handle with given value. template <class T, TF_DataType datatype> Status TestScalarTensorHandle(AbstractContext* ctx, const T value, AbstractTensorHandle** tensor) {
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README.md
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docs/en/data/github_sponsors.yml
url: https://github.com/cryptapi - login: codacy avatarUrl: https://avatars.githubusercontent.com/u/1834093?v=4 url: https://github.com/codacy - login: scalar avatarUrl: https://avatars.githubusercontent.com/u/301879?v=4 url: https://github.com/scalar - - login: ObliviousAI avatarUrl: https://avatars.githubusercontent.com/u/65656077?v=4 url: https://github.com/ObliviousAI - - login: databento
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tensorflow/c/eager/immediate_execution_context.h
// Abstract interface to a context. // // A context is responsible for creating key objects such as Tensors, // TensorHandles & Operations. class ImmediateExecutionContext : public AbstractContext { public: // Optimized scalar creation functions virtual AbstractTensorInterface* CreateInt64Scalar(int64_t value) = 0; virtual AbstractTensorInterface* CreateUint64Scalar(uint64 value) = 0;
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docs/en/docs/tutorial/response-model.md
You can use **type annotations** the same way you would for input data in function **parameters**, you can use Pydantic models, lists, dictionaries, scalar values like integers, booleans, etc. === "Python 3.10+" ```Python hl_lines="16 21" {!> ../../../docs_src/response_model/tutorial001_01_py310.py!} ``` === "Python 3.9+"
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tensorflow/c/c_api.cc
if (dst->dims() != 0) { return InvalidArgument( "Malformed TF_RESOURCE tensor: expected a scalar, got a tensor with " "shape ", dst->shape().DebugString()); } *dst = tensorflow::Tensor(tensorflow::DT_RESOURCE, dst->shape()); if (!dst->scalar<tensorflow::ResourceHandle>()().ParseFromString( string(static_cast<const char*>(tensor_interface->Data()),
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RELEASE.md
* For C++ API users: `IsLegacyScalar` and `IsLegacyVector` are now gone from `TensorShapeUtils` since TensorFlow is scalar strict within Google (for example, the shape argument to `tf.reshape` can't be a scalar anymore). The open source release was already scalar strict, so outside Google `IsScalar` and `IsVector` are exact replacements. * The following files are being removed from `tensorflow/core/public/`:
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